Trend analysis for hydration monitoring

ABSTRACT

A method, system, or device for hydration monitoring using trend analysis. The method may include measuring a physiological measurement of an individual with a sensor to obtain trend data correlating to a hydration level of the individual. The method may include removing an abnormal shift or an abnormal change from the trend data. The method may include removing abnormal shifts and changes from the raw trend data. The method may include identifying a trend parameter for the trend data, the trend parameter indicating a change in the hydration level of the body. The method may include determining a reference value for the trend parameter. The method may include identifying a change in the trend data for the trend parameter, the change in the trend data indicating a change in the hydration level of the individual.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/242,923, filed Oct. 16, 2015, the entire contents of which areincorporated by this reference.

BACKGROUND

Dehydration is a condition in which fluid in a living body decreasesbelow a living body's normal functioning level. Dehydration can occurwhen an individual is exercising for extended periods of time, intakeslittle or no fluids, or an ambient temperature rises to a point where anindividual cannot excrete enough sweat to cool their body and maintain anormal body temperature. Individuals that regularly exert themselves inhigh humidity environments, high-temperature environments, or forextended periods of time are prone to experience dehydration or symptomsof dehydration. Elderly individuals and children are also prone toexperiencing dehydration or symptoms of dehydration.

In less severe cases of dehydration, an individual's ability to performtasks will begin to deteriorate. For example, in the case of longdistance endurance athletes, an athlete that becomes dehydrated by aloss of as little as 2% body weight may begin to have their performanceimpaired. Losses in excess of 5% of body weight can decrease thecapacity of an individual to perform a task by as much as 30%.

Another group of people in danger of dehydration are individuals withdysentery. Dysentery is a gastrointestinal disorder characterized byinflammation of the intestines, particularly the colon. Diarrhea andvomiting are typical side effects of patients with dysentery, especiallyin the case of small children. As dysentery patients suffer fromdiarrhea and vomiting, they are unable to maintain a proper hydrationlevel, causing them to become dehydrated. Such a dehydrated stateexasperates the patient's condition, causing their health to furtherdeteriorate and prolong recovery time.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will be understood more fully from the detaileddescription given below and from the accompanying drawings of variousembodiments of the disclosure. The drawings, however, should not betaken to limit the disclosure to the specific embodiments, but are forexplanation and understanding only.

FIG. 1 depicts an electronic device according to one embodiment.

FIG. 2 illustrates an underside view or interior view of the electronicdevice according to one embodiment.

FIG. 3 depicts a top and a bottom perspective of an electronic deviceattached to a wrist of an individual according to one embodiment.

FIG. 4 depicts a side view of an electronic device according to oneembodiment.

FIG. 5 depicts an electronic device according to one embodiment.

FIG. 6 depicts an electronic device in direct communications with acomputing device according to one embodiment.

FIG. 7 depicts an electronic device and a computing device in indirectcommunication using a communications network according to oneembodiment.

FIG. 8 depicts a body area network (BAN) devices communicating using aBAN according to one embodiment.

FIG. 9 illustrates a schematic view of an electronic device according toone embodiment.

FIG. 10 is a block diagram of the electronic device with a correlator, abaseliner, and an alerter according to one embodiment.

FIG. 11 is a block diagram of a networked device that communicates withan electronic device according to one embodiment.

FIG. 12 illustrates a diagram of a method for trend analysis accordingto one embodiment.

FIG. 13 illustrates an example of a trend fault template for generationof trend alarms and their corresponding recommendations according to oneembodiment.

FIG. 14 illustrates another example of a trend fault template forgeneration of trend alarms and their corresponding recommendationsaccording to one embodiment.

FIG. 15A illustrates an impedance trending graph of measurement datataken using a sensor of an electronic device during a high activityphase and a low activity phase of a user of the electronic deviceaccording to one embodiment.

FIG. 15B illustrates physiological adjustment of a body part of the userwhen an electronic device is attached to the body according to oneembodiment.

FIG. 15C illustrates an impedance trending graph of measurement datataken during a high activity phase according to one embodiment.

FIG. 15D illustrates an impedance trending graph of measurement datataken during a low activity phase according to one embodiment.

FIG. 16A illustrates an impedance trending graph of measurement datataken by the electronic device during a transition phase according toone embodiment.

FIG. 16B illustrates an impedance trending graph of measurement datataken by the electronic device during a transition phase according toone embodiment.

FIG. 16C illustrates an impedance trending graph of measurement datataken by the electronic device during a transition phase according toone embodiment.

FIG. 17A illustrates an impedance trending graph of measurement datataken by the electronic device during a wetting phase according to oneembodiment.

FIG. 17B illustrates an impedance trending graph of measurement datataken by the electronic device when an impedance pad of the electronicdevice during a non-wet phase according to one embodiment.

FIG. 17C illustrates an impedance trending graph of measurement datawith a transition phase according to one embodiment.

FIG. 17D illustrates an impedance trending graph of measurement datawith an acclimation phase and transition phases and according to oneembodiment.

FIG. 18 illustrates an impedance trending graph for measurement datawith noise in the measurement data according to one embodiment.

FIG. 19 illustrates an impedance trending graph for measurement datawith outlying data points 1910 in the measurement data according to oneembodiment.

FIG. 20 illustrates an impedance trending graph for measurement datawith a discontinuity in the measurement data according to oneembodiment.

FIG. 21 illustrates an impedance trending graph of measurement data withdevice removal events and a device re-attachment event according to oneembodiment.

FIG. 22 illustrates a graphical user interface (GUI) to set trendingparameters of trending data according to one embodiment.

FIG. 23 illustrates a GUI to set trending parameters of trending datafor a trending graph according to one embodiment.

FIG. 24 illustrates a GUI to set trending parameters of trending datafor a trending graph according to one embodiment.

FIG. 25 provides an example illustration of a processing devicedisclosed herein, such as a user equipment (UE), a base station, anelectronic device (UMD), a mobile wireless device, a mobilecommunication device, a tablet, a handset, or other type of wirelessdevice according to one embodiment.

FIG. 26 illustrates a diagrammatic representation of a machine in theexample form of a computer system within which a set of instructions,for causing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed.

DESCRIPTION OF EMBODIMENTS

Dehydration, if left untreated or unaddressed, will progressively becomeworse until an individual is in a serious dehydrated condition.Typically when at least one-third of the fluid in a living body is lost,a temperature regulating function of the living body ceases to workcorrectly. This dysfunction causes a body temperature of the living bodyto increase, which causes the fluid level in the living body to befurther reduced. The increase in body temperature and reduced fluidlevel can cause an uncontrolled spiral. As the living body becomes moredehydrated, illnesses such as heat cramping, heat stroke, heatexhaustion, desert syndrome, and heatstroke can occur. In severe casesof dehydration, organs do not function properly or may fail entirely.

The embodiments and examples described herein are methods, systems, anddevices for monitoring and determining a hydration condition of anindividual. In one example, the hydration condition is a measure of ahydration level of the individual compared to an average hydration levelor typical hydration level of the individual, e.g., a baseline hydrationlevel of the individual. The hydration levels can include: hyponatremia(i.e., overhydrated), fully hydrated, partially dehydrated, and severelydehydrated. In another example, the hydration condition is an indicatorof symptoms level of the individual experiencing various degrees ofhydration. The symptom levels can include: dry mouth for slightdehydration, physical fatigue or mental fatigue for mild dehydration,sunken eyes or shriveled and dry skin for severe dehydration, and soforth.

An electronic device can monitor trending of measurement information todetermine a hydration condition of a user. The electronic device can bea wearable measurement device with processing logic coupled to a sensorinterface. The processing logic that includes hardware (e.g., circuitry,dedicated logic, programmable logic, microcode, etc.), software (e.g.,instructions executed by a processing device), firmware, or acombination thereof. The sensor can also be coupled to an impedancesensor. The sensor interface can use the impedance sensor to takeimpedance measurements. The processing logic can monitor changes (e.g.,trending) the impedances measurements taken from a user. A change in thetrending of the impedance measurements can indicate a change or shift ofa hydration level of the user. One advantage of monitoring a hydrationcondition of a user is that dehydration can be detected in the earlystages before an individual's performance levels are impacted or theybecome dangerously dehydrated. For example, typically people are unawareof dehydration symptoms at early stages, based on self-awareness. As aresult, dehydration symptoms can go untreated until an individual feelsdizzy or weak and realizes something is wrong. By the time an individualfeels dizzy or weak, they are already in a serious dehydrationcondition. When an individual reaches a serious dehydration condition,medical treatment may be required and the window to treat the individualmay become severely shortened. Monitoring a user's hydration conditioncan detect early stages of dehydration and alert the user or third partyto respond to a change in the user's hydration condition.

FIG. 1 depicts an electronic device 110 according to one embodiment. Inone embodiment, the electronic device 110, also referred to herein as awearable user measurement (UMD) device, can be a wristband, a headband,an armband, a chest band, a leg band, an ankle band, a strap, a garment,piece of clothing (such as a shirt), an accessory, or other object thatcan be shaped to attach or couple to an individual at a desired locationon the individual. In another embodiment, the electronic device 110 canbe integrated into other wearable objects such as a hard hat, a safetyharness, a safety lockout device, shoes, a bag, and so forth. In anotherembodiment, the electronic device 110 can be a computer device orprocessing device that is incorporated into an accessory worn on thebody or an item of clothing.

In one example, a desired location can be a location on the individualthat is comfortable for the individual to wear the electronic device 110for an extended period of time, such as a 24-hour period. For example,as many individuals are accustom to wearing wristwatches, a desiredlocation of the electronic device 110 may be the wrist. In anotherexample, a desired location is a location on the individual that willprovide an optimal or higher measurement accuracy level than otherlocations, such as a location on the individual that is the mostsensitive to a selected physiological measurement. For example, theunderside of the wrist may be an optimal location to make physiologicalmeasurements. In one embodiment, the electronic device 110 can includean impedance sensor or an optical sensor. The underside of the wrist canbe an optimal location because a user's veins and arteries are closer tothe skin surface than at many other locations on the body. When theelectronic device 110 takes impedance measurements or light spectroscopymeasurements to monitor a hydration condition, the blood of the user canbe used to take non-invasive impedance measurements or lightspectroscopy measurements. The veins and arteries may be closer to theskin surface can increase the accuracy of the measurements. Theelectronic device 110 can be shaped to attach to the individual at adesired location.

The electronic device 110 can include a housing 115 with an innercompartment or chamber. The chamber can include space to house: a sensorarray 120, a sensor 130, a display 140, a processing device 150, amemory device 160, a communication device 170, and/or a batterymanagement system (BMS) 180. In one embodiment, the processing device150 can include a sensor interface coupled to the sensor array 120 orthe sensor 130.

The housing 115 can be hermetically sealed, e.g., airtight, waterproof,sweat proof, dust proof, and so forth. In another example, the housingcan be a unibody (e.g., a single unit), where components such as thesensor 130 can be sealed with the unibody. In another embodiment, thehousing 115 can include multiple pieces, such as a first housing pieceand a second housing piece, that are sealed together to form ahermetically sealed housing 115.

In one example, the electronic device 110 can be an invasive deviceattachable to (or implantable within) a body of a user to obtaininvasive physiological measurements from the user. In another example,the electronic device 110 can be a non-invasive device attachable to thebody of the user to obtain non-invasive measurements from the user.

The electronic device 110 can include a sensor 130 or a sensor array 120with one or more sensors 130. In one example, the sensor 130 or thesensor array 120 can be integrated into the electronic device 110. Inanother example, the sensor 130 or the sensor array 120 can be coupledto the sensor interface of the processing device 150. In one example,the sensor 130 can be a physiological sensor. The physiological sensorcan include: a pulse oximeter, an electrocardiography (ECG) sensor, afluid level sensor, an oxygen saturation sensor, a body temperaturesensor (skin temperature or core temperature), a plethysmographicsensor, a respiration sensor, a breath rate sensor, a cardiac sensor, anoptical sensor, an impedance sensor, a bio-impedance sensor, aspectrometer, a heart rate sensor, a blood pressure sensor, or otherphysiological sensors. In another example, the sensor 130 can be aNewtonian sensor. The Newtonian sensor can include: a two-dimensional(2D) accelerometer, a three-dimensional (3D) accelerometer, a gyroscope,a magnetometer, a vibration sensor, a force sensor, a pedometer, astrain gauge, and so forth. In another example, the sensor 130 can be alocation sensor, a plethysmograph sensor, a breath sensor, or an oxygenlevel measurement. The location sensor can include: a global positioningsystem (GPS); a triangulation system; and so forth. In another example,the sensor 130 can be an environmental sensor. The environmental sensorcan include: a humidity sensor, an ambient temperature sensor, analtitude sensor, a barometer, a weather sensor, and so forth. In oneembodiment, the sensor 130 can be a non-invasive sensor. In oneembodiment, one or more of the physiological sensors, the Newtoniansensors, or the environmental sensors can be integrated into theelectronic device 110 or physically coupled to the electronic device110. In another example, one or more of the physiological sensors, theNewtonian sensors, or the environmental sensors can be physicallyseparated from the electronic device 110 and can communicate data withthe electronic device, either directly or indirectly as discussedherein.

The electronic device 110 can include a display 140 to show informationto a user or a third party based on the measurements from the sensor 130or the sensor array 120. In one embodiment, the display 140 can show thetime, e.g., a clock. In another embodiment, the information shown on thedisplay 140 may include measurement information, such as: a heart rateof an individual, a breathing rate of the individual, a blood pressureof the individual, a bio-impedance level of the individual, a hydrationlevel of the individual, a hydration score or index indicating anoverall hydration condition of the individual, a performance score orindex indicating an overall effect of a hydration condition on physicalor mental performance of the individual, and so forth. In anotherexample, the information shown on the display 140 may includerecommendations, such as: a recommendation to take a break; arecommendation to go home; a recommendation to go to a hospital; arecommendation to drink fluids; or other recommendations. In anotherexample, the information shown on the display 140 may include alerts,such as: a medical alert that the user may be experiencing a medicalepisode; an alert to take medication; an alert that an environmentadjacent the user may not be safe; an alert that the user has fallendown; or other alerts. In another example, the information shown on thedisplay 140 may include: health status information, health riskinformation, medication information, and other information. In oneexample, the alert can be sent another device (such as a caregiver orsupervisor's device). The alert can be a text message, email, telephonecall, and so forth.

In another embodiment, the display 140 can display information to a useror a third party based on information from other devices incommunication with the electronic device 110. For example, theelectronic device 110 can receive information from an automobile or asmart home device of a user or a third party. In this example, theinformation from the automobile or the smart home device can includeambient temperatures, humidity information, weather information, and soforth. The electronic device 110 can display the information from theautomobile or the smart home device or use it in combination withmeasurements taken using the sensor 130 or the sensor array 120 todetermine and display other information, such as a hydration level ofthe user.

In another example, the electronic device 110 can communicateinstructions to other devices based on sensor measurements of thedevice. For example, the electronic device 110 can take skin temperaturemeasurements using a skin temperature sensor of the electronic device110 and determine that an individual's skin temperature indicates thattheir body temperature exceeds or is below a body temperature range.When the individual's body temperature exceeds or is below a bodytemperature range, the electronic device 110 can determine that theambient temperature for the location of the individual is exceeded or isbelow an ambient temperature range. The electronic device 110 can sendan instruction to another device, such as a heating, ventilating, andair conditioning control unit (HVAC), that request that the other deviceadjusts the ambient temperature to be within an ambient temperaturerange.

In another embodiment, the processing device 150 can determine an errorwith the sensor 130 or the sensor array 120 and display the error to theuser or the third party using the display 140. For example, theprocessing device 150 can determine that the sensor 130 or the sensorarray 120 is not interfacing with the user properly and can use thedisplay 140 to display an error message to the user. In one embodiment,the sensor 130 or the sensor array 120 is not interfacing with the userproperly when the sensor 130 or the sensor array 120 is only partiallycontacting the body of the individual or is not completely contactingthe body of the individual. In another embodiment, the sensor 130 or thesensor array 120 is not interfacing with the user properly when anobject or particle is interfering with the processing device 150 usingthe sensor 130 or the sensor array 120 to take physiologicalmeasurements of the user, environmental measurements, Newtonianmeasurements, or other measurements. In one example, the processingdevice 150 can determine that object or particle is interfering withtaking measurements when measurement information is outside a definedmeasurement range or there is a discontinuity in the measurementinformation that exceeds a threshold level for the discontinuity. Forexample, when dirt comes between the sensor 130 or the sensor array 120and the body of the user, the dirt can cause a discontinuity in themeasurement information. When the processing device 150 determines thediscontinuity in the measurement information, the processing logic canuse the display 140 to display an error message associated with thediscontinuity.

In another embodiment, the sensor 130 or the sensor array 120 is notinterfacing with the user properly when the electronic device 110, thesensor 130, or the sensor array 120 has become dislocated or displaced.For example, measurements taken using the sensor 130 or the sensor array120 with a first orientation can have a higher accuracy level thanmeasurements taken using the sensor 130 or the sensor array 120 with asecond orientation. In one example, the first orientation is anorientation where the user is wearing the electronic device 110 in acorrect orientation and the second orientation is an orientation whenthe electronic device 110 has slipped or shifted to a differentorientation. When the electronic device 110 has slipped or shifted thesecond orientation, the processing device 150 identifies that ameasurement is outside a defined measurement range or there is adiscontinuity in measurement information and uses the display 140 todisplay an error message associated with the slippage or shifting.

In one example, the display 140 can be a touch screen display, such as acapacitive touch screen or a resistive touch screen. In another example,the display 140 can display a graphical user interface (GUI) to receiveinformation. In another example, the electronic device 110 can include adata port 165, such as a universal serial bus (USB) port, a mini-USBport, a micro-USB port, a Lightning® port, and so forth. In anotherexample, the electronic device 110 can include a wireless communicationsdevice 170 (as discussed in the proceeding paragraphs) to send orreceive information.

The processing device 150 can analyze or process measurements, receivedinformation, user input data, and/or other types of data. In oneexample, the electronic device 110 can monitor stress on a respiratorysystem of the individual. For example, the electronic device 110 can usethe sensor 130, such as an oxygen saturation sensor, to monitor thestress on a respiratory system of the individual.

In another example, the electronic device 110 can use one or moresensors 130 in the sensor array 120 to monitor stress on a plurality ofsystems of an individual, such as a biological system or a body system.The biological system may include a respiratory system, a cardiovascularsystem, a nervous system, an integumentary system, a urinary system, anexcretory system, a digestive system, an immune system, an endocrinesystem, a lymphatic system, a muscular system, a skeletal system, areproductive system, and other systems. The body system may include twoor more organs working together in the execution of a specific bodilyfunction, e.g., a neuroendocrine system, a musculoskeletal system, etc.For example, the electronic device 110 can monitor stress on the cardiacsystem of an individual using a blood pressure sensor of the sensorarray 120 and can monitor the stress on the respiratory system of theindividual using an oxygen saturation sensor of the sensor array 120.

The electronic device 110 can monitor biological systems, organs, bodyparts, body system, or other areas of an individual. In one example, theelectronic device 110 can monitor or aggregate stress measurements fromthe sensors of the sensor array with other measurements, such as a lungcapacity of an individual, a hematocrit (HCT), an oxygen saturationlevel, and/or other medical measurements. In another example, theelectronic device 110 can analyze the aggregated measurements todetermine stress on one or more biological systems, organs, body parts,and/or body system and use the aggregated measurements to determinemedical, health, hydration, and/or safety conditions.

In one example, the electronic device 110 can use the sensor array 120to monitor a medical condition of an individual, such as a cardiaccondition, under various environments or conditions for continuous,semi-continuous, or a periodic period of time on a long-term orprotracted basis. In one example, sensor measurements can be collectedusing the sensor 130 in the sensor array 120. In another example, thesensor measurements can be stored on a non-tangible computer readablemedium device 160 (e.g., a memory device) coupled to the processingdevice 150 or in communication with the processing device 150.

The battery management system (BMS) 180 can include: one or morebatteries (such as a rechargeable battery), a charger, and a managementdevice. The management device can manage and control power, e.g., powerto and from the one or more batteries or regulate power of theelectronic device 110. For example, the management device can directpower received from an external power source, such as wall outlet, viathe data port 165 (e.g., a USB port) and can recharge the one or morebatteries. The BMS 180 can include a wireless power system with awireless power coil to receive power. The management device can directpower received via the wireless power system to the one or morebatteries. In one example, the management device can direct power tocomponents or systems of the electronic device 110, such as the sensorarray 120, the sensor 130, the display 140, the processing device 150,the memory device 160, and/or the communication device 170. In anotherexample, the management device can be a processor or another processingdevice, independent of the processing device 150, that can manage andcontrol the power. In another example, the management device can besoftware executed by the processing device 150 or processing logic tomanage the power.

In one embodiment, the BMS 180 can determine when a charge level the oneor more batteries are below a threshold amount and can send anotification to the user indicating that the electronic device 110 needsto be charged. In one example, the electronic device can send thenotification to the user using a sensory device such as a vibrator, aspeaker, a display, and so forth.

FIG. 2 illustrates an underside view or interior view of the electronicdevice 210 according to one embodiment. The electronic device 210 cantake selected measurements using one or more sensors 280 and aprocessing device 250, according to one embodiment. The electronicdevice 210 may also include one or more indicators 290 used to alert theuser of the electronic device 210 of an event. The indicator 290 may belocated at various locations of the electronic device 210 based on atype of the indicator 290. For example, the indicator 290 can be adisplay or light located on the top of the electronic device 210. Inanother example, the indicator 290 can be a vibrator on the bottom ofthe electronic device 210. In another example, the indicator 290 can bea speaker integrated within the electronic device 210. The processingdevice 250 may receive measurement information from the one or moresensors 280 and can analyze the measurement information to determineselected information, such as physiological information, medicalinformation, safety event information, alert information, and so forth.In one example, the selected information can be hydration levelinformation, cardiac information (e.g., blood pressure or heart rate),blood oxygen level information, skin luminosity information, or otheruser information.

FIG. 3 depicts a top and a bottom perspective of an electronic device310 attached to a wrist 320 of an individual according to oneembodiment. The electronic device 310 may be located on the wrist of anindividual and may take one or more measurements at the wrist location.In one example, the electronic device 310 can cover or wrap around thecircumference of the wrist 320 of the individual. In another example,the electronic device can be a detachable device that can be coupled orattached to a holder (such as a wristband or chest strap).

FIG. 4 depicts a side view of an electronic device 410 according to oneembodiment. The electronic device 410 may be the same as the electronicdevices as shown in figures discussed herein. The electronic device 410can include one or more integrated sensors 420. In one example, theelectronic device can have a circular shape, a square shape, or othershapes. In another example, the electronic device 410 can have a flattop portion 430 and a circular remaining portion 440 to fit the contouror shape of a wrist on an individual. An advantage of the electronicdevice 410 fitting to contours of the wrist can be to align the sensors420 of the electronic device 410 with a desired location on the wrist ofthe individual (such as a bottom, side, or top of the wrist). Anotheradvantage of the electronic device 410 fitting to contours of the wristcan be to provide and/or maintain proper contact between the sensor 420of the electronic device 410 and a body of the user. The location of thesensors 420 is not intended to be limiting. The sensor 420 can belocated at different locations on the electronic device 410.Additionally, a shape of the electronic device 410 is not intended to belimiting. The electronic device 410 can be a variety of differentshapes, such as oval, circular, rectangular, and so forth. The locationon a body of the user that the electronic device 410 attaches is notintended to be limiting, as discussed in the preceding paragraphs.

FIG. 5 depicts an electronic device 510 according to one embodiment. Theelectronic device 510 can be a substantially circular band with an outersurface 520 and an inner surface 530. In one example, the outer surface520 or the inner surface 530 can be made of a flexible or non-rigidmaterial, such as rubber, polyurethane, and so forth. In anotherexample, the outer surface 520 or the inner surface 530 can be made of asemi-rigid or rigid material, such as plastic, metal, and so forth. Inanother example, a portion of the outer surface 520 or the inner surface530 can be the flexible or non-rigid material and a portion of the outersurface 520 or the inner surface 530 can be the semi-rigid or rigidmaterial. In another example, a portion of the inner surface 530 thatcontacts a body of the user is a conductive material. For example, oneor more sensors 582 are bio-impedance sensors that are conductive rubberpads that contact the body of the user and are used by processing logicto make bio-impedance measurements.

In one example, a cavity or chamber 540 can be between the outer surface520 and an inner surface 530. The cavity or chamber 540 can includemodules, units, systems, subsystems, or devices of the electronic device510. For example, the cavity or chamber 540 can house a power source550, a graphical user interface or touch controller 560, a communicationunit 570, a controller 580, one or more sensors 582, and/or other units.The communication unit 570 can wirelessly communicate with an externalelectronic device 590. The power source 550 can provide power to otherunits or modules of the electronic device 510. The touch controller 560can receive user input from an input device. In one example, the inputdevice can be a graphical user interface (GUI) or a touch display and beoperable to receive input via the GUI or the touch display. The inputdevice can receive communications from other devices via a communicationnetwork (e.g., a wireless network) or a communication connection (suchas a universal serial bus). The controller 580 can control systems andsubsystems of the electronic device 510.

The power source 550 can be a battery, such as a rechargeable battery.The power source 550 can receive power from another power source such asvia a cord plugged into a power source or using wireless power such asinductive wireless charging or resonant wireless charging. Theelectronic device 510 can include one or multiple sensors 582 (e.g., asensor array). In one example, the multiple sensors 582 can be differenttypes of sensors.

The electronic device 510 can receive safety event information and/orhealth information of a user of the electronic device 510 from anotherdevice. For example, the electronic device 510 can have a touchcontroller 560 to receive user input safety event information and/orhealth information. In one example, the power source 550, the touchcontroller 560, the communication unit 570, the controller 580, or theone or more sensors 582 can be in direct or indirect communication witheach other. For example, the touch controller 560 receives user inputinformation from the input device and communicates the user inputinformation to the controller 580. In this example, the controller 580includes a processor or processing device to analyze or process the userinput information. In another example, the sensor 582 can take aphysiological measurement and communicate physiological information tothe external electronic device 590 via the communication unit 570. Inone embodiment, the external electronic device 590 is an electronicdevice with a processor, such as a smartphone, electronic tablet, orpersonal computer. In another embodiment, the external electronic device590 is a cloud computing system or a server. The external electronicdevice 590 can analyze or process data or information received from theelectronic device 510. In one example, the external electronic device590 can store the processed data or information. In another example, theexternal electronic device 590 can send the processed data orinformation back to the electronic device 510.

FIG. 6 depicts an electronic device 610 in direct communications with acomputing device 620 according to one embodiment. In one example, sensormeasurements collected and/or stored by the electronic device 610 can beprocessed or analyzed by a processor of the electronic device 610 and/orby a computing device 620 in communication with the electronic device610. The electronic device 610 can be in direct communication 630 withthe computing device 620. In one example, the direct communication 630can be a Bluetooth® communication link, a Zigbee® communication link,radio signal, or other direct communication systems. In another example,the other computing device 620 can be a server that stores information,such as sensor measurements or safety event information previously takenby the electronic device 610 or sensor measurements or safety eventinformation taken from a group of individuals, as discussed herein. Inanother example, the computing device 620 can be a mobile computerdevice, such as a laptop computer, tablet, or a smartphone. Theelectronic device 610 can communicate information, such as sensormeasurements or safety event information, to the computing device 620.In one example, the computing device 620 can process and/or analyze thesensor measurements and/or information received from the electronicdevice 610. In another example, the computing device 620 can sendprocessed data, analyzed data, measurement results, and/or otherinformation to the electronic device 610. In another example, thecomputing device 620 can communicate calibration information to theelectronic device 610.

FIG. 7 depicts an electronic device 710 and a computing device 720 inindirect communication using a communications network according to oneembodiment. In one embodiment, the electronic device 710 can be astandalone device with a processing device to analyze or process:information taken from one or more sensors 750 of the electronic device710; information received from other devices; and/or information storedin a memory of the electronic device 710.

In another embodiment, the electronic device 710 communicates locallywith the computing device 720 use a wireless communication network 730or a cellular communication network 740. The local computing device 720can be a smartphone, tablet device, personal computer, laptop, a localserver, and so forth. In another embodiment, the electronic device 710communicates with a non-local or remote computing device 720 using awireless communication network 730 or a cellular communication network740. The non-local or remote computing device 720 can be a remoteserver, a cloud-based server, a back-end server, or other remoteelectronic devices.

In one example, the wireless communication network 730 is a cellularnetwork employing a third generation partnership project (3GPP) release8, 9, 10, 11, or 12 or Institute of Electronics and Electrical Engineers(IEEE) 802.16p, 802.16n, 802.16m-2011, 802.16h-2010, 802.16j-2009,802.16-2009. In another example, the electronic device 710 may provide asecure wireless area network (WLAN), a secure PAN, or a wirelessfidelity Private Wireless Wide Area Network (PWAN) to communicate withthe computing device 720. The electronic device 710 in the WLAN may usethe Wi-Fi® technology and IEEE 802.11 standards defined by the Wi-FiAlliance® such as the IEEE 802.11-2012, IEEE 802.11ac, or IEEE 802.11adstandards. Alternatively, the electronic device 710 and the computingdevice 720 in the WLAN may use other technologies and standards.Similarly, the electronic device 710 in the PAN or WPAN may use aBluetooth® technology and IEEE 802.15 standards defined by the BluetoothSpecial Interest Group, such as Bluetooth v1.0, Bluetooth v2.0,Bluetooth v3.0, or Bluetooth v4.0 (including Bluetooth low energy).Alternatively, the electronic device 110 in the secure PAN may use othertechnologies and standards. In another embodiment, the communicationsnetwork may be a Zigbee® connection developed by the ZigBee Alliancesuch as IEEE 802.15.4-2003 (Zigbee 2003), IEEE 802.15.4-2006 (Zigbee2006), IEEE 802.15.4-2007 (Zigbee Pro). The WAN or PWAN can be used totransmit data over long distances and between different LANs, WLANs,metropolitan area networks (MANs), or other localized computernetworking architectures.

The electronic device 710 and the computing device 720 can be inindirect communication using a communications network such as wirelesscommunication network 730 (such as a Wi-Fi® network) and/or using acellular communication network 740 (such as a 3rd Generation PartnershipProject (3GPP®) network) to communicate data or measurement information.In one example, the electronic device 710 can take sensor measurementsusing a sensor 750 and communicate the sensor measurements to thecomputing device 720 via the wireless communication network 730 and/orthe cellular communication network 740. In another example, thecomputing device 720 can receive sensor measurements from the electronicdevice 710 via the wireless communication network 730 and/or thecellular communication network 740 and process the sensor measurementsand/or analyze the sensor measurements. When the computing device 720has processed the sensor measurements and/or analyzed the sensormeasurements, the computing device 720 can communicate the processedsensor measurements, analyzed sensor measurements, sensor measurementresults, or other information to the electronic device 710 via thewireless communication network 730 and/or the cellular communicationnetwork 740.

FIG. 8 depicts a body area network (BAN) devices 862-876 communicatingusing a BAN according to one embodiment. In one embodiment, the BAN caninclude a wired body area network, a wireless body area network (WBAN),and/or a body sensor network (BSN). The BAN can include multiplewearable computing devices or wearable sensor devices 862-876 that arein communication with each other to send and receive data andinformation. In one example, the BAN devices can include: a BAN device860 that is attached or coupled to the body of the user; an BAN device862 that is implanted into the body of the user; a BAN device 868 thatis embedded into the body of a user; a BAN device 870 that is mounted ona surface of the body, and so forth. In another example, the BAN devicescan include devices adjacent the user including: a BAN device 864 shapedto fit in a clothes pockets of the user, a BAN device 866 that a usercan carry, such as a handheld device; a BAN device 872 that isintegrated into clothes of the user; a BAN device 876 located in auser's bag, a BAN device 874 integrated into a user's bag, and so forth.In one embodiment, an electronic device is a BAN device. In anotherembodiment, the BAN devices 862-876 can be body sensor units (BSUs) thatinclude a processing device, a sensor, and a communication device. TheBSUs can communicate with a body central unit (BCU) 878 that is a hubfor the BAN devices. The BCU 878 can be located at any of the locationsdiscussed above for the BAN devices 862-876. The BCU 878 can include aprocessing device, memory, a communication device, and a display. TheBCU 878 can receive data from a BAN device 862-876 and analyze the data.In one example, the BCU 878 can display the analyzed data using thedisplay of the BCU 878. In another example, the BCU 878 can send theanalyzed data to a BAN device 862-876 or another device. One advantageof the BCU 878 communicating with the BAN devices 862-878 is that theBAN devices 862-878 can be configured to be minimal sensor devices withlow power consumption and a compact design where the BCU 878 performsthe processing of the data.

In another embodiment, the BCU 878 can be a data hub or data gateway tomanage the BAN devices 862-876. In another embodiment, the BCU 878 canprovide a user interface to control the BAN devices 862-876. In anotherembodiment, the BAN devices 862-876 and/or the BCU 878 can use wirelessprivate area networks (WPAN) technology as a gateway or relay to reachlonger ranges. In one example, the BCU 878 can us a WPAN to connect theBAN device 862-876 on the body to the internet. For example, medicalprofessionals can access patient data from the BAN devices 862-876online using the internet independent of a location of a patient.

FIG. 9 illustrates a schematic view of an electronic device 910according to one embodiment. The electronic device 910 may include theindicators 918, a sensor array 922 (to include at least one of thesensors in FIG. 1, 2, 4, 5, or 7), a processing device 955, acommunications interface 960, an antenna 962 coupled to thecommunications interface 960, external sensors 964, and accompanyingantenna(s) 966. In one example, the sensor array 922 may include one ormore physiological sensors to take physiological measurements (e.g.,measurements related to the body of the individual or animal). Thesensor array 922 may include one or more sensors to engage a user of theelectronic device to take measurements. In various examples, the sensorarray 922 may include, without limitation: a bio-impedance spectroscopysensor 968 (or simply impedance sensor 968), an optical sensor 970, anelectrocardiogram (ECG) sensor 972, a temperature sensor 974 (such as athermometer or thermistor), an accelerometer 976, a spectrometer 978, asweat rate sensor 980, and so forth. In one example, the sensor array922 can contact or engage the body of the user at a location 982.

FIG. 10 is a block diagram of the electronic device 1010 with acorrelator 1013, a baseliner 1015, and an alerter 1017 according to oneembodiment. The electronic device 1010 may include, without limitation,one or more physiological sensor(s) 1002, one or more Newtoniansensor(s) 1004, one or more environmental sensor(s) 1005, one or morelocation sensor(s) 1004, a processor 1003, a memory device 1008, adisplay 1080, a communication interface 1090 (such as a radio frequency(RF) circuit), and an antenna 1092 coupled to the communicationinterface 1090.

In one embodiment, the communication interface 1090 may communicate, viathe antenna 1092, with an external electronic device 590 (FIG. 5), acomputing device 620 or 720 (FIGS. 6 and 7), and with other wirelessdevices such as electronic devices 1010 of other users. In one example,the communication interface 1090 may communicate the information using acellular network, a wireless network, or a combination thereof. In oneexample, the communications network can be a cellular network employinga third generation partnership project (3GPP) release 8, 9, 10, 11, or12 or Institute of Electronics and Electrical Engineers (IEEE) 802.16p,802.16n, 802.16m-2011, 802.16h-2010, 802.16j-2009, 802.16-2009. Inanother example, the electronic device 110 may provide a secure wirelessarea network (WLAN), a secure PAN, or a wireless fidelity (Wi-Fi)Private Wireless Wide Area Network (PWAN) to communicate with a device.The electronic device 110 in the WLAN may use the Wi-Fi® technology andIEEE 802.11 standards defined by the Wi-Fi Alliance® such as the IEEE802.11-2012, IEEE 802.11ac, or IEEE 802.11ad standards. Alternatively,the devices in the WLAN may use other technologies and standards.Similarly, the electronic device 110 in the PAN or WPAN may use theBluetooth® technology and IEEE 802.15 standards defined by the BluetoothSpecial Interest Group, such as Bluetooth v1.0, Bluetooth v2.0,Bluetooth v3.0, or Bluetooth v4.0. Alternatively, the electronic device110 in the secure PAN may use other technologies and standards. Inanother embodiment, the communications network may be a Zigbee®connection developed by the ZigBee Alliance such as IEEE 802.15.4-2003(Zigbee 2003), IEEE 802.15.4-2006 (Zigbee 2006), IEEE 802.15.4-2007(Zigbee Pro). The WAN or PWAN can be used to transmit data over longdistances and between different LANs, WLANs, metropolitan area networks(MANs), or other localized computer networking architectures.

In one embodiment, the electronic device 1010 can communicate data withthe other devices via another device, such as a smartphone or tabletcomputing device. For example, the communication interface 1090 can pairwith a smartphone via the wireless network. The smartphone can receivedata using the wireless network and can communicate the data to theother device. In another embodiment, the electronic device 1010 maycommunicate information with the other device via repeaters or a relaysystem. For example, a user of the electronic device 1010 can be outsideof a coverage area for the cellular network or the wireless network,e.g., a farm worker out in the field. In this example, the electronicdevice 1010 can determine that it is outside the coverage area andswitch to communicating via the repeaters or the relay system.

In one embodiment, the electronic device 1010 can determine it isoutside a coverage area when it does not receive a signal from thecellular network or the wireless network. In another embodiment, theelectronic device 1010 can ping the cellular network or the wirelessnetwork (such as a tower within the cellular network or the wirelessnetwork) and determine that it is outside the coverage area when theelectronic device 1010 does not receive a reply to the ping. In anotherembodiment, multiple electronic devices 1010 can communicate with eachother to form a piconet. In this embodiment, a first electronic devicecan determine it is outside the coverage area and can scan for a secondelectronic device, where the second electronic device is in the coveragearea or in communication with another electronic device in the coveragearea. When the first wearable safety finds the second electronic device,the first electronic device can communicate information to an end deviceor to the cellular network or the wireless network via the secondelectronic device.

The processor 1003 may include a first sensor interface 1007 forreceiving sensor data from the physiological sensor(s) 1002, a secondsensor interface 1008 for receiving sensor data from the Newtoniansensor(s) 1004, a third sensor interface 1009 for receiving sensor datafrom the environmental sensor(s) 1005, a fourth sensor interface 1010for receiving sensor data from the location sensor(s) 1006, and aprocessing element 1011. The processing element 1011, in turn, mayinclude a correlator 1013, a baseliner 1015 and/or an alerter 1017. Thememory device 1008 may also include, without limitation, a sensor module1016, physiological data 1024, environmental data 1026, Newtonian data1028, and profile data 1030, location data 1032.

The electronic device 1010 may include the sensor array 120 (FIG. 1)with two or more sensors. In the depicted embodiment, the electronicdevice 1010 may include one or more physiological sensors 1002, one ormore Newtonian sensors 1004, one or more environmental sensors 1005, oneor more location sensors 1006, or a combination thereof. In someinstances, the Newtonian sensors 1004 may be physiological sensors. Thatis, in some embodiment, the activity level may be determined from one ormore physiological measurements.

A physiological measurement may be any measurement related to a livingbody, such as a human's body or an animal's body. The physiologicalmeasurement is a measurement made to assess body functions.Physiological measurements may be simple, such as the measurement ofbody or skin temperature, or they may be more complicated, for examplemeasuring how well the heart is functioning by taking an ECG(electrocardiograph). Physiological measurements may also include motionand/or movement of the body. In some cases, these physiologicalmeasurements may be taken as an aggregate, e.g., as physiological data,with which to correlate to other physiological measurements, aphysiological parameter, and/or an environmental parameter.

A parameter may be considered a measurable quantity (such as heart rate,temperature, altitude, and oxygen level, as just a few examples). Whenmeasurements of parameters are taken in the aggregate, the measurementsmay form data which may be analyzed and correlated to other data orparameters, to identify trends or to identify when meeting (orexceeding) certain thresholds that trigger alerts or other actions andthe like.

The physiological sensors 1002 may include a pulse oximeter sensor, anelectrocardiography (ECG) sensor, a fluid level sensor, an oxygensaturation sensor, a body core temperature sensor, a skin temperaturesensor, a plethysmographic sensor, a respiration sensor, a breath ratesensor, a cardiac sensor (e.g., a blood pressure sensor, a heart ratesensor, a cardiac stress sensor, or the like), an impedance sensor(e.g., bio-impedance spectroscopy sensor), an optical sensor, aspectrographic sensor, an oxygen saturation sensor, or a sweat ratesensor. Alternatively, other types of sensors may be used to measurephysiological measurements, including measurements to determine activitylevels of a person wearing the electronic device.

The Newtonian sensors 1004 may be any of the physiological sensorsdescribed above, but in some cases, the Newtonian sensors 1004 areactivity or motion sensors, such as, for example, a gyroscope sensor, avibration sensor, an accelerometer sensor (e.g., a sensor that measuresacceleration and de-acceleration), a three-dimensional (3D)accelerometer sensor (e.g., sensors that measure the acceleration andde-acceleration and the direction of such acceleration andde-acceleration), a force sensor, a pedometer, a strain gauge, amagnetometer, and a geomagnetic field sensor that may be used foractivity level measurements; whereas the physiological sensors 1002 maybe used for specific physiological measurements.

In one embodiment, an environmental measurement may be any measurementof an area approximate or adjacent a user. The environmental sensors1005 may be a humidity sensor, an ambient temperature sensor, analtitude sensor, a barometer, and so forth. A location measurement maybe any measurement of a location of the user or a movement of the user.The location sensor 1006 may be a global positioning system (GPS), atriangulation system, or a location sensor. One or a combination of thephysiological data 1024, the environmental data 1026, the Newtonian data1028, the profile data 1030, and the location data 1032 may be obtainedfrom other sources such as through the network 115 from sourcesreachable in the cloud or online.

In another embodiment, the environmental measurement can be anymeasurement of a local or central location measurement of where a useris located. For example, one or more environmental sensors 1005 may belocated at a location within a threshold radius of the user, such as athreshold radius from the user location. In this example, theenvironmental sensors 1005 can take environmental measurements and relaythe information to the electronic device 1010 or to a communication hubthat has a communication channel established with the electronic device1010. Alternatively, the environmental sensors 1005 can takeenvironmental measurements and relay the information to a processing hubthat can analyze the environmental measurements to determine selectedenvironmental factors (such as a humidity level, a heat index, and soforth) and can communicate the environmental factors to the electronicdevice 1010 or to another electronic device. In another embodiment, theprocessing hub can receive the environmental measurements from theenvironmental sensors 1005 and other measurements (such as physiologicalmeasurements) from the electronic device 1010. The processing hub cananalyze the environmental measurements and the other measurements todetermine selected result data, such as a hydration level of a user or ahealth level of the user. In another embodiment, the electronic device1010 can take a first set of environmental measurements and the localenvironmental sensors 1005 can take a second set of environmentalmeasurements. The first set of environmental measurements and the set ofenvironmental measurements can be combined or aggregated and theprocessing hub and/or the electronic device 1010 can analyze theaggregated environmental measurements.

In another embodiment, the environmental measurements can be from anenvironmental information outlet or provider. For example, theenvironmental information outlet or provider is a weather station, anews station, a television station, an online website, a computer orsoftware application (such as a weather channel application or a fitnessapplication for a smartphone or tablet computer), and so forth. Theelectronic device 1010 or the processing hub can receive theenvironmental information from the environmental information outlet orprovider can use the environmental information to determine selectedphysiological and/or environmental data or factors.

The first sensor interface 1007 may be coupled to the one or morephysiological sensors 1002, a second sensor interface 1009 may becoupled to the one or more Newtonian sensors 1004, a third sensorinterface 1009 may be coupled with the one or more environmental sensors1005, and a fourth sensor interface 1010 may be coupled with the one ormore location sensors 1006. The processing element 1011 may be operableto execute one or more instructions stored in the memory device 1008,which may be coupled with the processor 1003. In some cases, theprocessing element 1011 and memory device 1008 may be located on acommon substrate or on a same integrated circuit die. Alternatively, thecomponents described herein may be integrated into one or moreintegrated circuits as would be appreciated by one having the benefit ofthis disclosure. The memory device 1008 may be any type of memorydevice, including a non-volatile memory, volatile memory, or the like.Although not separately illustrated the memory device may be one or moretypes of memory configured in various types of memory hierarchies.

The memory device 1008 may store physiological data 1024, such ascurrent and past physiological measurements, as well as profile data1030, including user profile data, bibliographic data, demographic data,and the like. The physiological data 1024, and in some cases the profiledata 1030, may also include processed data regarding the measurements,such as statistical information regarding the measurements, as well asdata derived from the measurements, such as predictive indicators,results, and/or recommendations.

In one example, the profile data 1030 may also include informationconnected to user profiles of the users that wear the electronic devices1010, such as a gender of the user, an age of the user, a body weight ormass of the user, a health status of the user, a fitness level of theuser, or a family health history of the user. In another example, theprofile data 1030 can include occupational information of the users thatwear the electronic devices 1010, such as a job type, a job title,whether the job is performed indoors or outdoors, a danger level of thejob, and so forth. For example, the job types can include an elderlylive-at-home job, an oil driller, a construction worker, a railroadworker, a coal mine worker, a job in confined spaces, a fireman, aconstruction worker, an outdoor worker, an office worker, a truckdriver, a child, or a disabled individual.

In one example, the electronic device 1010 can receive the profile data1030 via a touch screen device integrated into the electronic device1010 or coupled to the electronic device 1010. In another example, theelectronic device 1010 can receive the profile data 1030 via acommunication port of the electronic device 1010. For example, theelectronic device 1010 can receive profile data 1030 from another devicevia a wired communication connection (e.g., a universal serial bus) orvia a wireless communication connection (e.g., a Bluetooth®communication technology).

The profile data 1030 may also be linked to various physiological data1024 and Newtonian data 1028 and be tracked over time for the users. Theprofile data 1030 may also include baselines of physiological parametersfor respective users. In one example, the baselines are of a heart rate,a blood pressure, bio-impedance, skin temperature, oxygen levels,hydration levels, electrolyte levels and so forth. When the baselinesare included with the user profiles, the user profiles may be referredto as baseline profiles for the respective users.

The memory device 1008 may also store one or a combination of theenvironmental data 1026, the Newtonian data 1028, the profile data 1030,and the location data 1032. The Newtonian data 1028, environmental data1026, or location data 1032 may be current and past measurements, aswell predictive data for predictive modeling of activity levels,environmental levels, or locations. The memory device 1008 may storeinstructions of the sensor module 1016 and instructions and data relatedto the correlator 1013, the baseliner 1015 and the alerter 1017, whichperform various operations described below.

In particular, the sensor module 1016 may perform operations to controlthe physiological sensors 1002, Newtonian sensors 1004, environmentalsensors 1005, and location sensors 1006, such as when to turn them onand off, when to take a measurement, how many measurements to take, howoften to perform measurements, etc. For example, the sensor module 1016may be programmed to measure a set of physiological measurementsaccording to a default pattern or other adaptive patterns to adjust whenand how often to take certain types of measurements. The measurementsmay be stored as the physiological data 1024, the environment data 1026,and the Newtonian data 1028, location data 1032, and some of them mayalso be integrated as a part of the profile data 1030, as discussed.

In the depicted embodiment, the processing element 1007 (e.g., one ormore processor cores, a digital signal processor, or the like) executesthe instructions of the sensor module 1016 and those related to thecorrelator 1013, the baseliner 1015, the alerter 1017 and possibly othermodules or routines. Alternatively, the operations of the sensor module1016 and the correlator 1013, the baseliner 1015, and the alerter 1017may be integrated into an operating system that is executed by theprocessor 1003. In one embodiment, the processing element 1011 measuresa physiological measurement via the first sensor interface 1007. Theprocessing element 1011 may measure an amount of activity of theelectronic device 1010 via the second sensor interface 1009. The amountof activity could be movement or motion of the electronic device 1010(e.g., by tracking location), as well as other measurements indicativeof the activity level of a user, such as heart rate, body temperature,skin luminosity, or the like. The processing element 1011 measures anenvironmental measurement via the third sensor interface 1009. Theprocessing element 1011 measures a location measurement via the fourthsensor interface 1010.

In one embodiment, the Newtonian sensors 1004 may include a hardwaremotion sensor to measure at least one of movement or motion of theelectronic device 1010. The processing element 1011 may determine theamount of activity based the movement or motion of the electronic device1010. The hardware motion sensor may be an accelerometer sensor, agyroscope sensor, a magnetometer, a GPS sensor, a location sensor, avibration sensor, a 3D accelerometer sensor, a force sensor, apedometer, a strain gauge, a magnetometer, and a geomagnetic fieldsensor.

The processor 1003 may further execute instructions to facilitateoperations of the electronic device 1010 that receive, store and analyzemeasurement data, environmental data, location data, and profile data.The indicator(s) 1018 may include one or more of a light, a display, aspeaker, a vibrator, and a touch display, usable to alert the user totake actions in response to trending levels of: physiological parametersduring or after physical activity and/or prepare for undertakinganticipated physical activity; environmental parameters; activityparameters, or location parameters.

In some embodiments, for example, the correlator 1013 may analyzemeasurement data to correlate physiological data, environmental data,activity data, location data, or user experienced feedback with aphysiological parameter, environmental parameter, activity parameter, alocation parameter, or user experienced feedback to predict a change ina level of the physiological parameter, environmental parameter,activity parameter, or a location parameter. In one embodiment, the userexperienced feedback can be physiological or psychological symptomsexperienced by the user. For example the physiological or psychologicalsymptoms can include: headaches, dizziness, tiredness, mental fatigue,increased thirst, dry mouth, swollen tongue, physical weakness,confusion, sluggishness, and so forth.

Such prediction may enable timely and accurate recommendations to a userin terms of hydrating, adjusting effort levels or other specific actionsto address a trend or a change in the physiological parameter, theenvironmental parameter, the activity parameter, or the locationparameter. The recommendations may be displayed in the display 1080,sent via an alert through one of the indicator(s) 1018 or displayed inanother device such as a smartphone or tablet or other computing device.

In another embodiment, the correlator 1013 may also track and analyzeNewtonian data of the user related to physiological or determinedparameters (such as heart rate, oxygenation, skin luminosity, hydration,and the like), related to location and type of activity (such asactivity levels associated with being at the gym, riding a bike,attending class, working at a desk, sleeping, or driving in traffic, andthe like) and/or related to scheduling information (such as appointmentson a calendar, invites received from friends, or messages related totravel and/or activity plans, and the like). Through this analysis, theelectronic device 1010 may track activity data over time, intelligentlyand continuously (or periodically) analyze all of this information, andalert the user through the indicator(s) 1018 to take a specific actionat a proper time before a start of a safety event. The specific actionmay include to hydrate extra hours before physical activity and to eatat least two hours before any physical activity, or other such timingthat may be general to most users, or customized to a training ornutrition routine of a specific user.

In another embodiment, the correlator 1013 can build an individualizedprofile for the user. The correlator 1013 can receive the individualizedprofile information from an input device of the electronic device 1010.For example, the correlator 1013 can receive the individualized profileinformation from a touch screen of the electronic device 1010. Inanother example, the correlator 1013 can receive the individualizedprofile information from a device in communication with the electronicdevice (such as via a USB port or using a Bluetooth® technology). Inanother embodiment, the electronic device 1010 can include a memory thatstores the individualized profile information for the user.

The individualized profile can include physiological informationassociated with the user. For example, the physiological information caninclude an average heart rate of the user, an age of the user, a healthlevel of the user, and so forth. The individualized profile can alsoinclude information associated with a location or environment that theuser is located. For example, the individualized profile can include:humidity level information, such as when the user is located in a dryclimate or in a humid climate; altitude level information, such as whenthe user is located at a relatively high altitude or a relatively lowaltitude; seasonal information, such as if it is winter where the useris located or summer. The correlator 1013 can also determine anenvironmental effect on the user for the location where the user islocated at. For example, if the user is located at their home that is ata high altitude with a dry climate and it is a winter season, thecorrelator 1013 can determine that the user is acclimated to highaltitudes, dry climates, and the winter season. The correlator 1013 canalso update the user profile when the user changes location. Forexample, when the user leaves their home location and goes on a vacationto a location that is at a low altitude, a humid climate, and it is asummer season, the correlator 1013 can determine that the user is notacclimated to the low altitude, humid climate, and summer season.

In one embodiment, the electronic device 1010 can alert the user of thechanges to the individualized profile. In another embodiment, theelectronic device 1010 can alert the user of the changes to effectsassociated with the changes to the individualized profile. For example,the electronic device 1010 can access a table of predetermining effectsof the user changing their user profile. In one example, the table canindicate that when the user switches from a low altitude to a highaltitude location, the user may experience altitude sickness. In anotherexample, the table can indicate that when the user switches from a dryclimate to a humid climate location, an ability of the user's body tocool itself down when an ambient temperature is relatively high. Inanother embodiment, the table can indicate when the current user profileindicates safety risks or physiological performance changes.

In another embodiment, the individualized profile can also includeinformation associated with clothing or apparel worn by the user of theelectronic device 1010. For example, the individualized profile canindicate that a user may wear different types of apparel for differentenvironments including: a thickness of fabric; a type of a fabric, suchas wool or cotton; a number of clothes layers worn by the client;accessories worn by the client, such as hard hats, steeled toed shoes,safety goggles, safety belts, and so forth; and gender types of apparel,such as women and men's apparel. In one example, the correlator canadjust measurement information or measurement results based on thedifferent types of clothing or apparel. For example, the correlator 1013can determine that the user is a firefighter and is wearing multiplelayers of clothing to protect against fire. In this example, thecorrelator 1013 can determine that a cause of a hydration level of theuser decreasing is the multiple layers of clothing cause the firefighterto sweat more and lose more fluid than a typical number of layers ofclothing worn by the user.

In one embodiment, the alerter 1017 may decide the most appropriatetiming and mode of alert, whether through one of the indicator(s) 1018,the display 1080 or another device such as a smartphone, tablet or thelike. The type of indicator used to alert the user may also becustomized to or by the user.

In one embodiment, the correlator 1013 may determine a correlationbetween different data points or data sets of the input data (such asdata collected from different sensors, devices, or obtained from thecloud or online). The correlator 1013 may determine different types ofcorrelations of the data points or data sets. In one example, thecorrelator 1013 may execute a Pearson product moment correlationcoefficient algorithm to measure the extent to which two variables ofinput data may be related. In another example, the correlator 1013 maydetermine relations between variables of input data based on asimilarity of rankings of different data points. In another example, thecorrelator 1013 may use multiple regression algorithms to determine acorrelation between a data set or a data point that may be defined as adependent variable and one or more other data sets or other data pointsdefined as independent variables. In another example, the correlator1013 may determine a correlation between different categories orinformation types in the input data.

In further examples, when the correlator 1013 determines a correlationbetween the different data points or data sets, the correlator 1013 mayuse the correlation information to predict when a first event orcondition may occur based on a second event or condition occurring. Inanother example, when the correlator 1013 determines a correlationbetween the different data points or data sets, the correlator 1013 mayuse the correlation information to determine a safety event. Asdiscussed in the preceding paragraphs, a safety event can be an eventthat negatively impacts a user's safety or health. For example, a safetyevent can be: a fall event where a user falls down, such as an elderlyperson falling down; an unconscious event where a user becomesunconscious, such as a farm worker losing consciousness due to heatstroke; a cognitive ability reduction event where a user's cognitiveability is reduced, such as when a user becomes confused or disorientedfrom low blood pressure or low blood oxygenation levels; a fatigue eventwhere a user becomes fatigued, such as when a user is fatigued fromdehydration; a sickness event where a user becomes physically sick; andso forth. In another example, when the correlator 1013 determines acorrelation between the different data points or data sets, thecorrelator 1013 may use the correlation information to determine a causeof a condition and/or event, such as a safety event.

Additionally, or alternatively, the correlator 1013 may determine acorrelation between physiological data 1024, environmental data 1026,Newtonian data 1028, profile data 1030, and location data 1032. Forexample, the input data may include hydration level data (physiologicaldata) and ambient temperature data (environmental data). In thisexample, the correlator 1013 may identify a correlation between anincrease in the ambient temperature, a decrease in a hydration level ofa user, and a heat stroke safety event. The correlator 1013 may identifythe correlation between the ambient temperature, the hydration level,and the heat stroke by using a regression algorithm with the heat strokeas an independent variable and the ambient temperature and the hydrationlevel as dependent variables. When the correlator 1013 has identifiedthe correlation between the heat stroke, the ambient temperature, andthe hydration level, the correlator 1013 may predict a heat strokesafety event based on a change in a hydration level of a user or a rateof change of a hydration level of a user and a change in the ambienttemperature or a rate of change in the ambient temperature.

Additionally, or alternatively, the correlator 1013 may determine acorrelation between a fatigue event, an altitude level, and anoxygenation level of a user. For example, the correlator 1013 maydetermine a correlation between an increase in the altitude level, adecrease in the oxygenation level of the user, and an increase in afatigue event. When the correlator 1013 determines the correlationbetween the altitude level, the oxygenation level, and the fatigueevent, the correlator 1013 may predict an increase or decrease in aprobability of a safety event based on a change in the oxygenation levelof the user and the altitude level at which the user is currently at. Inone example, the correlator 1013 can use the individualized profileinformation (as discussed in the preceding paragraphs) of the user todetermine the predicted increase or decrease in the probability of asafety event. For example, the correlator 1013 can determine a change inaltitude level of the user from a relatively low altitude to arelatively high altitude. The correlator 1013 can use the individualizedprofile information to determine that the user is acclimated to therelatively high altitude (such as if they live at a high altitude) andadjust the predicted increase or decrease in the probability of a safetyevent for the change in altitude in view of the individualized profileinformation. For example, the correlator 1013 can predict that thechange from the low altitude to the high altitude will not increase ordecrease the probability of the safety event occurring.

In a further example, the correlator 1013 may identify a correlationbetween location information and physiological data of a user. Forexample, the correlator 1013 may determine a location of a user for at aperiod of time, such as by using GPS sensor data or triangulation sensordata. In this example, the correlator 1013 may receive physiologicalmeasurement data (such as heart rate measurement data, opticalspectroscopy data, hydration level measurement data, blood pressuremeasurement data, and so forth). The correlator 1013 may correlate thelocation of the user with the physiological measurement data to increasean accuracy of data analysis, a diagnosis, or result data and/or provideadditional details regarding a cause of a safety event.

In one example, the correlator 1013 may determine that a user is at workin an office location. When the correlator 1013 detects an increase in aheart rate or a blood pressure of a user, the correlator 1013 maycorrelate heart rate or blood pressure data and the location informationto determine a cause of the cognitive ability reduction event. Forexample, when a heart rate or blood pressure of an individual increaseswhile at a work in an office, the correlator 1013 may determine that theheart rate or blood pressure increase may be due to psychological causes(such as stress) rather than physiological causes (such as exercising orworking out) because the user is at a location where an individual isnot likely to physically exert himself or herself.

In another example, the correlator 1013 may determine an occupation ofthe user, such as by using the profile data 1030. In one embodiment, thecorrelator 1013 can determinate that the occupation of the user is ahigher risk occupation (e.g., a statistically more dangerousoccupation). For example, the correlator 1013 can access a database orlist (stored at the memory device 1008 or externally) that includesinformation associated with an occupation, such as a danger level or asafety event likelihood. When the correlator 1013 detects that theoccupation of the user is a higher risk occupation (e.g., an occupationwith a risk level that exceeds a threshold value), the correlator 1013may correlate heart rate data, blood pressure data, hydration leveldata, with the occupational information to determine a cause of a safetyevent. For example, when a heart rate and blood pressure of anindividual increases and a hydration level of the individual decreaseswhile the individual is working at an oil refinery or on a farm, thecorrelator 1013 may determine that the heart rate or blood pressureincrease may be due to physiological influences of the occupation (suchas strenuous labor or no breaks) rather than psychological causes (suchas stress) because the occupation where the individual is working at islikely to include physical exertion.

In a further example, the correlator 1013 may use multiple regressionalgorithms to determine a correlation between multiple data points ordata sets and safety events. For example, the correlator 1013 mayreceive heart rate data, skin temperature, bio-impedance data, skinluminosity and hydration level data of a user. In this example, thecorrelator 1013 may determine a correlation between these types ofphysiological data and a dehydration event of the individual. Forexample, the physiological data could be from optical spectroscopy (skinluminosity) and/or bio-impedance data. The correlator 1013 may thendetermine that as the bio-impedance of an individual increases and skinluminosity decreases, a probability of a dehydration event occurringincreases.

Additionally, or alternatively, the correlator 1013 may filter out acorrelation determination (e.g., a determination that data points ordata sets and safety events may be correlated) when a correlation levelis below a threshold level. For example, when the correlator 1013determines that there may be a 30 percent correlation between a skintemperature or a bio-impedance level of an individual and a fall event,the correlator 1013 may filter out or disregard the correlationinformation when determining a cause of the fall event. In anotherexample, the correlator 1013 can use a learning algorithm or machinelearning to determine when to filter out a correlation determination.For example, at a first instance of a fall, there may be a 30 percentcorrelation between a skin temperature or a bio-impedance level of anindividual and a fall event The correlator 1013 can monitor multiplefall events and use machine learning to determine that the initial 30percent correlation is actually a 60 percent correlation and adjust thefilter to not filter out the correlation between the skin temperature orthe bio-impedance level of an individual and a fall event or assign thecorrelation of the skin temperature or the bio-impedance level of anindividual and a fall event a different weight.

Additionally, or alternatively, the correlator 1013 may filter out thecorrelation determination based on a schedule of an individual. Forexample, when the correlator 1013 determines that an individual istaking a lunch break, off of work, or sleeping, the correlator 1013 mayfilter out safety events that are associated with the occupation of theuser, e.g., the correlator 1013 can filter out false positives.

Additionally, or alternatively, the correlator 1013 may discount orweight a correlation determination based on the correlation level of thecorrelation determination. For example, when the correlator 1013determines that there may only be a 30 percent correlation between anoccupation of an individual and a hydration level of an individual, thecorrelator 1013 may discount or assign a lower weight to the correlationdetermination (relative to a higher correlation percentage such as 90percent) when determining a cause of a safety event.

Additionally, or alternatively, the correlator 1013 may assign weightsto different factors, such as: physiological data 1024 (e.g., differenttypes or qualities of physiological parameters), environmental data 1026(e.g., different types or quality of environmental parameters),Newtonian data 1028 (e.g., different types or quality of Newtonianparameters), profile data 1030, location data 1032 (e.g., differenttypes or quality of location parameters), a time of day, and so forth.In one example, the correlator 1013 may assign a first weight tohydration level data of an individual and a second weight to profiledata of an individual when determining a probability of a safety eventfor an individual. In this example, when determining the safety eventprobability, the correlator 1013 may assign a higher weight to thehydration level data relative to the profile data, for example.

The correlator 1013 may additionally, or alternatively, usepredetermined weights for the physiological data 1024, environmentaldata 1026, Newtonian data 1028, profile data 1030, and location data1032. In another example, the correlator 1013 may receive user-definedor predefined weights from an input device indicating the weights forthe different physiological and/or environmental data. In anotherexample, the correlator 1013 may determine the weights to assign to thephysiological data 1024, environmental data 1026, Newtonian data 1028,profile data 1030, and location data 1032 based on correlation levels ofthe physiological data 1024, environmental data 1026, Newtonian data1028, profile data 1030, and location data 1032. For example, when acorrelation level between a safety event and a heart rate of anindividual may be relatively low over a threshold period of time and/orunder a threshold number of different conditions, the correlator 1013may assign a low weight to heart rate data when determining a cause of asafety event.

In one example, the correlator 1013 may assign different weights to oneor more of the physiological data 1024, environmental data 1026,Newtonian data 1028, profile data 1030, and location data 1032 based onother physiological data 1024, environmental data 1026, Newtonian data1028, profile data 1030, and location data 1032. For example, based on alocation of an individual, the correlator 1013 may assign a first weightto environmental data 1026 and a second weight to profile data 1030. Inanother example, the correlator 1013 may assign weights to differentsafety events.

Additionally, or alternatively, the correlator 1013 may useenvironmental data 1026 or location data 1032 to determine a cause of asafety event. For example, when a user is located at a fitness facilityworking out, the correlator 1013 may increase a weight for a physicalexertion-related safety event occurring because of in physical exertionof a user (such as an increase in a heart rate or decrease in ahydration level of a user). In another example, when a user is locatedat home in bed resting or sleeping, the correlator 1013 may correlate alocation of the user with safety events of the user. In this example,the correlator 1013 may determine that a decrease in the probability ofa safety event occurring due to an individual being is located in theirbedroom for a threshold period of time (e.g., a safer environment).

In one embodiment, the correlator 1013 can determine a weighting ofmeasurement information or physiological information using medicalevaluation information. In one example, the medical evaluationinformation includes medical evaluation information of the user, such asa medical physical. The medical evaluation information can include:medical history and health history information, such as whether the useris a smoker or a non-smoker; a user's blood pressure information;hereditary diseases information; a user's sexual health information; auser's dietary information, a user's exercise routine information, suchas how often the user exercises; a user's heart or lung examineinformation; and so forth. In one example, the correlator 1013 can usethe medical evaluation information to set initial weight for differentdata types. The correlator can update or adjust the weights for thedifferent data types using machine learning. For example, thephysiological data 1024, environmental data 1026, and Newtonian data1028 is assigned a first set of weights based on the medical evaluationinformation. As the electronic device 1010 uses the sensors to collectthe physiological data 1024, environmental data 1026, and the Newtoniandata 1028, the correlator 1013 can use the physiological data 1024, theenvironmental data 1026, and the Newtonian data 1028 to customize theweighting of the measurement information or physiological information tothe individual. For example, the correlator 1013 can receive medicalevaluation information for the user input device of the electronicdevice 1010 using an input device of the electronic device 1010.

The correlator 1013 may track, sort and/or filter input data. The inputdata may include: user schedule information, such as a daily schedule ofthe user; survey information, such as information received from surveysof individuals; research information, such as clinical researchinformation or academic research information associated with one or moresafety events of the electronic device; and so forth.

The correlator 1013 may use location-based tracking and/or schedulinginformation of the user in determining an expected or probable safetyevent occurring. For example, when a user is a member of a sports team,the user's schedule may include practice schedule information and/orgame schedule information. In this example, the correlator 1013 may usethe schedule information to anticipate that the user may beparticipating in physical activity and increase a probability that asafety event may occur.

The correlator 1013 may use timer information determining an expected orprobable safety event occurring. For example, the correlator can monitorhow long it may have been since a user took a break or consumed water.In this example, as the length of time increase between a break or waterconsumption, the probability that a safety event may occur increases. Inanother example, the correlator can use the timer information toperiodically request a response from the user. For example, when asafety event has not occurred within a threshold amount of time thatwould trigger a user response, the electronic device can request a userresponse from the user when the threshold amount of time has beenexceeded.

In another example, the correlator 1013 can have a work mode (the useris at work) and a home mode (the user is at home), where a type ofsafety event that the electronic device monitors for and/or aprobability of a safety event occurring can increase or decrease whenswitching between the work mode and the home mode. For example, when theuser has a high-risk occupation, the correlator 1013 can monitor forsafety events related to the high-risk occupation when the correlator isin a work mode and switch to monitoring for safety events related to lowrisk activities when the correlator is in a home mode.

In another example, the correlator 1013 may use the schedulinginformation in correlation with a location of the user to determine anexpected or probable safety event. For example, the schedulinginformation may indicate that the user may be scheduled to attend alecture at a physical fitness facility and the correlator 1013 mayadjust the types or probabilities of a safety event occurring in view ofthe scheduling information. In this example, while the correlator 1013may typically increase a probability of a safety event occurring for theuser in anticipation of physical activity based on the locationinformation (e.g., the physical fitness facility), the correlator 1013may adjust the adjust the types or probabilities of a safety eventoccurring in view of the scheduling information that the user may beattending a lecture rather than working out.

Additionally, or alternatively, the correlator 1013 may track and updateactivity levels of users and correlate these levels with safety eventsover time. For example, the GPS sensor of the electronic device 110 mayindicate that the user usually works out at the gym on Monday, Wednesdayand Friday at 7 a.m. and goes on a long bike ride on Saturday, usuallystarting about 8:30 a.m. Although these activities may not be availablewithin the scheduling information or data of the electronic device 110(or other tethered device), the correlator 1013 may execute machinelearning to add to a user's activity data these events that normallyoccur.

The electronic device 1010 may store historical or previous safety eventinformation of the user. In one example, the correlator 1013 may storethe historical information on the memory device 1008 of the electronicdevice 110. In another example, the correlator 1013 may use thecommunication device 170 (FIG. 1), the communication unit 570 (FIG. 5),or the communication interface 1090 to store the safety eventinformation on a memory device coupled to or in communication with theelectronic device, such as a cloud-based storage device or a memorydevice of another computing device. In another example, the correlator1013 may be part of a cloud-based system or the other computing device.

The correlator 1018 may filter and/or sort safety event information. Inone example, the correlator 1018 may receive a filter or sort commandfrom the electronic device or an input device to filter and/or sort thesafety event information. In another example, the filter or sort commandmay include filter parameters and/or sort parameters. The filterparameters and/or sort parameters may include different types of safetyevents.

In another example, the correlator 1013 may sort and/or filter the inputdata based on a trending of safety events. For example, the correlator1013 may sort safety events that may be trending in an increasingdirection or a decreasing direction and may sort the safety events basedon the trending. In this example, different safety events for a user maybe trending in different directions, such as a dehydration events of auser may be increasing in trending and fall events may be stable orstagnant.

In one example, the correlator 1013 may sort or filter the safety eventsdata on a group level. In another example, the correlator 1013 may sortor filter the safety events on an individual level.

In another embodiment, the baseliner 1015 may receive profileinformation from a new user to include any or a combination of gender,age, weight, health, fitness level, and family health histories. Thehealth and fitness levels of the user may be based at least in part onphysiological measurements received from the physiological sensor(s)1002 and the activity data received from the Newtonian sensors 1004. Thebaseliner 1015 may then identify, from a plurality of baseline profilesof other users (e.g., a group of users), a baseline profile that is mostsimilar to the user profile based on a correlation between the userprofile information and baseline profile information. The baselineprofiles can include baseline information of a probability of safetyevents occurring for a user. The user profiles can include informationof the types of safety event that may be probable to occur for the user.

The baseliner 1015 may then be able to set a baseline against which tojudge safety events. In an alternate embodiment, the baseline profilethat is most similar to the user profile is identified from anaggregated baseline profile for a plurality of individuals correspondingto the plurality of baseline profiles. Alternatively, or additionally,the most similar profiles may look at safety events that occur for theindividual as compared to the group. For example, the user may be mostsimilar to another individual because they both react physiologicallysimilarly to hot temperatures outside. In another example, the user mayhave a similar dehydration profile to the most similar profile, meaning,when the user works out the user may reach a dehydration level at acertain point in time that substantially matches the timing of themost-similar profile.

The electronic device 110 may further receive survey information and/orresearch information from an input device with which to build or add tothe user and/or baseline profiles. For example, the electronic device110 may receive survey information that includes: gender information,age information, physical weight information, general healthinformation, family information, fitness level information, and soforth. In one example, the correlator 1013 may determine a correlationbetween the survey information and user input data. For example, thecorrelator 1013 may correlate the age, weight, fitness level, andgeneral health level of a user with survey information from otherindividuals to determine a correlation between the survey informationfor the individual and the other individuals. In this example, thebaseliner 1015 may set a baseline for a measurement of the electronicdevice 110 for the individual based on baselines for the otherindividuals with the same or similar survey information.

In another example, the correlator 1013 may correlate the userinformation with research information (such as research papers, clinicalstudies, and so forth). For example, the electronic device may retrieveresearch information related to a physiological parameter, thecorrelator 1013 may then correlate the research information with safetyevents for the user to generate a research correlation. The baseliner1015 may then adjust the baseline set for the user related to the safetyevents in response to the research correlation.

The correlator 1013 can store safety event information in a safety eventdatabase 1012. In one embodiment, the correlator 1013 can determineparameters associated with a safety event (safety parameters). Thesafety parameters can include threshold values for measurements or datavalues, such as physiological sensor measurements, environmental sensormeasurements, Newtonian sensor measurements, location sensormeasurements, or profile data 1030. The correlator 1013 can store thesafety event and the associated safety parameters in the safety eventdatabase 1012. For example, the correlator 1013 can determine thatparameters for a heat stroke event can be a skin temperature above a100-degree temperature, blood pressure above 150 systolic, and abio-impedance level above 15000 ohms (e.g., a dehydration levelthreshold). In this example, the correlator 1013 can determine theseparameters can store the safety event with the associated parameters inthe safety event database 1012. In another example, the storepredetermined safety events with the associated parameters. In anotherexample, the safety event database 1012 can receive the safety eventsand the associated parameters from another device or server 1094.

The preceding examples are intended for purposes of illustration and arenot intended to be limiting. The correlator 1013 may identify acorrelation between various data points, data sets, data types, and/orsafety events. After having a correlation that informs, for example, aheat stroke event, the hydration level, and/or oxygenation level of theuser, and further in consideration of a present activity level of theuser, the alerter 1017 may alert the user at the proper time when tohydrate or how to moderate activity levels to avoid or minimize a safetyevent. The alerter 1017 may alert the user, a third party (such as acaregiver, a supervisor, or a coach), or a combination thereof.

FIG. 11 is a block diagram of a networked device 1100 that communicateswith an electronic device according to one embodiment. In oneembodiment, the network device 1100 may be a hub device (or basestation), an external electronic device 590 (FIG. 5), a computing device620 or 720 (FIGS. 6 and 7), and so forth. The network device 1100 cancommunicate with other wireless devices such as the electronic device110 (FIG. 1), the electronic device 220 (FIG. 2), the electronic device410 (FIG. 4), the electronic device 510 (FIG. 5), the electronic device610 (FIG. 6), the electronic device 710 (FIG. 7), the electronic device910 (FIG. 9), or the electronic device 1010 (FIG. 10). In anotherembodiment, the network device 1100 may be a server. As such, thenetworked device 1100 may include a communication interface 1160 whichwith to communicate over a communications network. In some cases, thecommunication may be with the help of a communication interface 1190(such as an RF circuit) and an antenna 1192, to communicate wirelessly,e.g., as may be used by the hub (or a base station or a switch or thelike that) at least in some implementations.

In one embodiment, the components of the networked device 1100 may bethe same or similar to the components discussed in the precedingparagraphs with reference to the electronic device 1010 of FIG. 10, withthe exception of the sensors 1002, 1004, 1005, and 1006 and indicator(s)1018. Sensor data may be sent from the electronic devices to thenetworked device over the network and through the communicationsinterface 1160. Furthermore, any alerts or information for the user maybe sent back to the electronic device to initiate one of theindicator(s) 1018 or provide information to the user through the display1080. The functions and capabilities of the electronic device 1010 ofFIG. 10 may generally be replicated or enhanced by the functions andcapabilities of the networked device 1100 in terms of processing power,data analytics, performing correlations, predictions, analyzing andsetting baselines, and other algorithmic work.

Accordingly, in one embodiment, the network device 1100 may alsoinclude, without limitation, a processor 1103, a memory device 1108, anda display 1180. The processor 1103 may include a processing logic or aprocessing element 1111 that may have a correlator 1114, a baseliner1115 and an alerter 1117. The memory device may include a sensor module1116, physiological data 1124, environmental data 1126, activity data1128, profile data 1130, and location data 1132 of and related to theusers.

In one embodiment, the sensors 1002, 1004, 1005, and 1006 of theelectronic device 1010 may generate measurements, information and datathat is stored in the memory device 1008 of the electronic device 1010as discussed herein. In another embodiment, the electronic device 1010may send this data and information to the networked device 1100 to bestored as the physiological data 1124, the environmental data 1126, theactivity data 1128, the profile data 1130, and/or the location data1132. The user profiles and, optionally, baseline profiles of the usersmay also be stored with the profile data 1130. And, as discussed herein,historical information may be stored by the networked device 1100 thatmay help the networked device 1100 with machine learning and to performmore intense processing and statistical analysis that may be required toimplement the processes and strategies disclosed herein.

For example, the processing element 1111 may function largely the sameas the processing element 1011 discussed with reference to theelectronic device 1010 of FIG. 10, but with perhaps greater processingpower and speed. In other words, the correlator 1114 may functionsimilarly to the correlator 1013 upon receipt of the physiological data,environmental data, activity data, profile data, and location data fromthe electronic device 110 and/or from other sources. Those other sourcesmay include cloud or internet servers that provide weather, altitude,geographic and location information, calendar and schedulinginformation, and other such information or data. Some of these sourcesmay include other devices of the user such as a smartphone, tablet, ascale, a refractometer, a plasma osmolality device or other computingdevice that may or may not be tethered to the electronic device 1010, inalternative embodiments.

Furthermore, the baseliner 1115 may function similarly to the baseliner1015 of the electronic device 1010 of FIG. 10 based on sensor data andother information received from the electronic device 1010 or the othersources. Additionally, the alerter 1117 may function similarly to thealerter 1017 of the electronic device 1010 of FIG. 10, based on sensordata and other information received from the electronic device 1010 orother sources.

In one embodiment, the electronic device 1010 may generate additionalphysiological data, environmental data, activity data, profile data, andlocation data from on-going measurements. The user may also enter newinformation that may change the user's profile, such as a new age or jobtype, and this information may also be sent by the electronic device1010 to the networked device 1100. When a baseline is set by theelectronic device 1010 for a user as discussed in the precedingparagraphs, this baseline may also be sent to the networked device 1100where baselines for all users may be kept up to date, for tracking thoseusers as well as providing data from which an initial baseline may beset for a physiological parameter of a newly added user. Furthermore, oralternatively, the baseline may be set by the baseliner 1115 of thenetworked device 1100 and sent to the electronic device of thecorresponding user against which new physiological measurements may becompared and/or correlated locally by the electronic device.

Furthermore, any updates to a user profile, new physiological and/orenvironmental measurements may be sent to the networked device 1100 bythe electronic device 1010. As this type of information is updated, thenetworked device 1100 may also update the baseline and/or baselineprofiles of the users as disclosed herein. Any such updated baseline maybe sent to the corresponding electronic device 1010 of the correct user,which electronic device 1010 may then update the baseline for that userlocally.

In one example, the electronic device can take physiologicalmeasurements of an individual. In another example, the electronic devicecan take environmental measurements of an environment adjacent theindividual. In another example, the electronic device can receiveenvironmental information or measurements from other devices. Forexample, the electronic device can receive ambient temperaturemeasurements and humidity measurements from the other device. In oneexample, the electronic device can use the measurements from the otherdevice to verify a validity of measurements taken using sensors of theelectronic device. In another example, the electronic device canincorporate the environmental information or measurements withmeasurements taken by the electronic device to be used for a statisticalanalysis.

The electronic device can perform the statistical analysis on thephysiological measurements and/or environmental measurements to detectmeasurement trend shifts to determine a hydration condition of a user.The electronic device can perform a statistical analysis on thephysiological measurements and/or environmental measurements to detectsymptoms indicative of a hydration condition of a user. The electronicdevice can identify trend changes, erroneous or outlying measurementdata, and sensor shifts. The electronic device can detect trend shiftsby computation of a slope in the trending of measurement data todetermine a hydration condition of the user.

The electronic device can use the trend analysis to detect changes intrend parameters without user input. When a change in a hydrationcondition of a user or dehydration symptoms is detected, the electronicdevice can generate a recommendation based on the change in thehydration condition or the dehydration symptoms. In one example, theelectronic device can use a sensory device to communicate therecommendation. In another example, the electronic device can use acommunication device to send the recommendation to another electronicdevice. One advantage of the electronic device using the trend analysiscan be to non-invasively monitor a user in the background (e.g., withoutneed of user input) and provide an alert or recommendation to a userwhen there is a change in a hydration condition of a user or dehydrationsymptoms are detected.

Trend parameters can be set to designate when a change in a hydrationcondition of a user or dehydration symptoms are detected for measurementdata from a sensor of the electronic device. In one example, theelectronic device can initially use raw measurement data for analysis.The electronic device can filter or remove outlining or rogue datapoints from the raw data during a detection analysis. The electronicdevice can also detect abnormal or sudden shifts and changes at recentdata point different trend parameter. The electronic device can evaluatechanges in trend parameters, according to predetermined relationships,in order to filter out spurious or erroneous data and provide accuraterecommendations for trend analysis. In another embodiment, the trendparameter is a coefficient of variation indicating an amount ofvariability relative to a reference value, such as a baseline valuedefined for a user.

The trend parameters can be in reference to a baseline (such as aparametric baseline). The baseline can be derived or generated (e.g.,empirically derived or generated) using dehydration or body fluidmodels. The electronic device can use multiple trend parameters tofilter measurement data. The trend parameters can be used as markersdifferent hydration condition indicators. For example, a first trendparameter can be set to indicate a change in an impedance level of auser and a second trend parameter can be set to indicate a change in anoptical measurement of a user.

FIG. 12 illustrates a diagram of a method 1200 for trend analysisaccording to one embodiment. The method 1200 may at least partially beperformed by processing logic that includes hardware (e.g., circuitry,dedicated logic, programmable logic, microcode, etc.), software (e.g.,instructions executed by a processing device), firmware or a combinationthereof. The method 1200 may be performed, at least in part, byprocessing logic of the electronic device 110 (FIG. 1), the electronicdevice 220 (FIG. 2), the electronic device 410 (FIG. 4), the electronicdevice 510 (FIG. 5), the electronic device 610 (FIG. 6), the electronicdevice 710 (FIG. 7), the electronic device 910 (FIG. 9), or theelectronic device 1010 (FIG. 10).

Referring to FIG. 12, the method 1200 begins with determining a trendparameter for raw trend data (1210). In one embodiment, a trendparameter can be a measurement parameter that is sensitive to changes ina hydration condition. In one example, the processing logic can use acycle to determine which parameters, such as impedance, opticalmeasurements, skin temperature, etc., of a user are sensitive to changesin the hydration condition of a user. When the selected trend parametersenable differentiation between high and low hydration conditions, theelectronic device can isolate faults or circumstance that may causechanges in the hydration condition. For example, a change in ambienttemperature, humidity levels, user exertion levels, health conditions,and so forth can cause an increase or decrease in trending data. In oneexample, trend changes may not be used in an overall trend analysisunless corresponding changes are seen in related trend parameters. Inthis example, only viable symptoms are provided. When viable symptomsare detected, specific recommendations can be generated.

The method can include removing outlying data points from the raw trenddata (1220). For example, the outlying data points can be removed fromthe raw trend data so that the outlying data points are not included inthe trend analysis. The method can include removing abnormal shifts andchanges from the raw trend data (1230). The method can includecalculating a reference value for the trend parameter (1240). The methodcan include evaluated changes in the trend data based on the trendparameter (1250).

FIG. 13 illustrates an example of a trend fault template for thegeneration of trend alarms and their corresponding recommendationsaccording to one embodiment. FIG. 14 illustrates another example of atrend fault template for the generation of trend alarms and theircorresponding recommendations according to one embodiment. On the leftside of the templates in FIGS. 13 and 14 are discrepancies correlatingto the various trend event parameters, including core temperature, skintemperature, impedance levels, spectroscopy measurements, sweat rate,etc. In one embodiment an actual trend signature indicating which shiftsare up or down is compared to the fault templates of FIGS. 13 and 14.

The resultant set of parameters is then scored against all possiblecauses, by applying varying degrees of importance to the match betweentemplate parameters and actual parameters. The electronic device canevaluate the score to determine a most probable cause of dehydration.The electronic device can perform the trend analysis to analyze trenddata and evaluate an overall hydration condition of a user. Oneadvantage of the electronic device using the trend analysis is toreliably detect and annunciate changes and shifts in trend data that areprobably hydration condition changes, and suppresses false or spuriousshifts in trend data.

In one embodiment, fault templates or parametric filtering areimplemented to detect shifts in trend data and to determinerecommendations based on those changes. In another embodiment, multipletrend parameters can be used to enhance classification of detected trendshifts into random or spurious events, sensor or signal shifts, oractual hydration condition changes.

Various embodiments have been shown and described herein to provideexamples. However, numerous variations, changes, and substitutions canoccur. For example, the control charts (FIGS. 13 and 14) may be replacedwith any statistical analysis which checks for variation beyond acertain value from the mean. In another example, the electronic devicecan analyze trend data to determine when a time ordered slope of trenddata exceeds a threshold. In another example, a magnitude of changes inone or more parameters could also be included as a factor inrecommendation generation.

FIGS. 15-25 illustrate impedance measurement trending under variousconditions. FIG. 15A illustrates an impedance trending graph 1500 ofmeasurement data taken using a sensor of an electronic device during ahigh activity phase 1520 and a low activity phase 1540 of a user of theelectronic device according to one embodiment. The impedance measurementtrending 1500 can have a variety of measurement phases, including: anacclimation phase 1510; the high activity phase 1520; a transition phase1530; the low activity phase 1540; and a removed device phase 1550. Theimpedance measurement trending may include a phase range, such as a highactivity phase range 1560 when the user is engaging in a relatively highphysical activity or a low phase range 1570 when the user is engaging inthe relatively low physical activity. The processing logic of theelectronic device can identify a measurement phase for trendinginformation. In another example, a phase range may be a coefficient ofvariation from a reference value 1580. The coefficient of variation maymeasure of range or spread that indicates an amount of variabilityrelative to the reference value 1580. For example, the coefficient ofvariation may be measured in relative standard deviations (RSD), where afirst RSD is a euhydration zone indicating a normal hydration zone of auser. A second RSD can indicate a hypohydration zone when the deviationis below the reference value or a hyperhydration zone when the deviationis above the reference value. The phase ranges, the coefficient ofvariation, and RSD are not intended to be limiting. The values andranges of the phase ranges, coefficients of variation, and RSDs mayvary. For example, different users may have different phase ranges,coefficients of variation, and RSDs.

The removed device phase 1550 can be when the electronic device isremoved from a body of the user. In one example, the electronic devicecan determine that it has been removed when the trending data ceases. Inanother example, the electronic device can determine that it has beenremoved when the trending data decreases to zero or near zero. Inanother example, the electronic device can determine that it has beenremoved when the trending data is stagnant or flat lined. In anotherexample, the electronic device can determine that it has been removedwhen the trending data becomes highly noisy.

The trending data can also include tags with information identifyingadditional information associated with a point in time or time periodsduring a measurement period. For example, the tags can include:refractometer measurement information; an impedance pad wetting status,such as whether the impedance pads were wetted before the electronicdevice was attached to the user; fluid intake or excretion, such asdrinking or urinating; and types of activities of the user, such asrunning, sleeping, or waking up.

Data points can be tagged with various icons symbolic of the event(e.g., a heart signifying information related to a heart rate, a toiletsignifying fluid excretion; a water bottle signifying fluid intake, anda running figure signifying a user exercised). In one example, theprocessing logic can tag event using measurement information. Forexample, the processing logic can tag a start of an exercise period ofthe user when an accelerometer measures an increase in activity of theuser. In another example, the electronic device can receive tagginginformation from an input device, such as via a graphical user interfacedisplayed on a touch screen.

FIG. 15B illustrates physiological adjustment of a body part 1594 of theuser when an electronic device 1590 is attached to the body according toone embodiment. For example, the electronic device 1594 can include oneor more impedance pads 1592 that the electronic device 1590 can use totake impedance measurements. The surface of the impedance pads 1592 canbe uneven or porous. When the impedance pads 1592 initially contacts thebody part 1594, there can be are gaps 1596 between the impedance pads1592 and the body part 1594. As the body part 1594 physiologicallyadjustments to the attachment of the impedance pads 1592 to the bodypart 1592, a surface 1598 (such as skin) of the body part 1594 can fillin the gaps 1596 over the acclimation phase 1510 (FIG. 15A) and canreturn to a relatively smooth surface over the de-acclimation phase 1530(FIG. 15A). The electronic device 1590 can monitor trending data duringthe acclimation phase to determine when the body part 1590 hasphysiologically adjusted. For example, when there are gaps between thesurface 1598 and the impedance pads 1592, the impedance pads 1592 maynot fully contact the surface 1598 of the body part 1594 causing a lowerconductivity level between multiple impedance pads 1592.

As the surface 1598 fills in the gaps 1596, the conductivity level canincrease because of the reduction or elimination of the gaps 1596. Inanother embodiment, the physiological adjustment of the body part 1590during the acclimation period 1510 can be caused by a change in bodytemperature, such as an increase in the body temperature, because theelectronic device 1590 and impedance pads 1592 capture and reflects heatemitted from the body part 1594. In another embodiment, thephysiological adjustment of the body part 1590 during the acclimationperiod can be caused by body 1594 adjusting to pressure on the surface1598 of the body part 1594. The preceding examples and embodiment of thephysiological adjustment of the body part 1590 during the acclimationperiod are not intended to be limiting. The physiological adjustment ofthe body part 1590 during the acclimation period can be caused by avariety of physiological reasons. The de-acclimation phase 1530 can bethe reverse of physiological adjustment described for the acclimationperiod 1510.

FIG. 15C illustrates an impedance trending graph 1522 of measurementdata taken during a high activity phase 1520 according to oneembodiment. In one embodiment, the impedance trending graph 1522 caninclude the low activity phase 1540, a transition phase 1530, and a highactivity phase 1520. The high activity phase 1520 can be a phase when auser is physically exerting himself or herself for a period of time. Forexample, the high activity phase 1520 can be when the user is running,jogging, working out, and so forth. In another embodiment, the highactivity phase 1520 can be a phase when a user is in a high dehydrationrisk environment for a period of time. For example, the high activityphase 1520 can be when the user is in a relatively high humidityenvironment, a relatively high ambient temperature environment, and soforth. In one example, the electronic device can determine that trendinginformation is for the high activity phase 1520 when a rate of change ofthe trending information is within a threshold range. In anotherexample, the electronic device can determine that trending informationis for the high activity phase 1520 when the trending information spansa defined range of impedance values, such as approximately 6500 Ohms (Ω)to 7500Ω.

In one embodiment, the electronic device can identify a phase of themeasurement data. The electronic device can use different trendingparameters for different phases to determine a hydration condition of auser. For example, the electronic device can identify that themeasurement data is for the high activity phase 1520. The electronicdevice can determine that when a trending of the measurement dataindicates an increase in impedance values, a user is becoming dehydrated(1526). The electronic device can determine that when a trending of themeasurement data indicates a decrease in impedance values, a user isbecoming hydrated (1528). In another example, the electronic device canuse an overall trending direction (1524) of the measurement data todetermine a hydration condition of the user.

In one embodiment, different measurement phases can have differentanalysis parameters. For example, the electronic device can use a firstparameter set when the trending information is for a high activity phase1520. In another example, the electronic device can use a secondparameter set when the trending information is for a low activity phase1540.

FIG. 15D illustrates an impedance trending graph 1542 of measurementdata taken during a low activity phase according to one embodiment. Thelow activity phase 1540 can be a phase when a user is not physicallyexerting himself or herself or minimally physically himself or herselffor a period of time. For example, the low activity phase 1540 can bewhen the user is sitting at a desk, driving, sleeping, lying down,eating lunch, and so forth. In one example, the electronic device candetermine that trending information is for the low activity phase 1540when a rate of change of the trending information is within a thresholdrange. In another example, the electronic device can determine thattrending information is for the low activity phase 1540 when thetrending information spans a defined range of impedance values, such asapproximately 2500 Ohms (Ω) to 5500Ω.

In one embodiment, the electronic device can identify a phase of themeasurement data. The electronic device can use different trendingparameters for different phases to determine a hydration condition of auser. For example, the electronic device can identify that themeasurement data is for the low activity phase. The electronic devicecan determine that when a trending of the measurement data indicates anincrease in impedance values, a user is becoming dehydrated (1526). Theelectronic device can determine that when a trending of the measurementdata indicates a decrease in impedance values, a user is becominghydrated (1528). In another example, the electronic device can use anoverall trending direction (1524) of the measurement data to determine ahydration condition of the user.

FIG. 16A illustrates an impedance trending graph 1600 of measurementdata taken by the electronic device during a transition phase 1630according to one embodiment. The transition phase 1630 is a phase whenthe trending data shifts from a high activity phase 1620 to a lowactivity phase 1640. The electronic device can analyze the trending datato determine that the user has ceased a high activity action (such asrunning) and is now performing a low activity action (such as sleeping).In one embodiment, as the body of the user physiologically adjusts fromthe high activity phase 1620 to the low activity phase 1640, thetrending data can decrease at a specified rate or a rate within aspecified range. Additionally or alternatively, as the body of the userphysiologically adjusts from the high activity phase 1620 to the lowactivity phase 1640, the trending data can decrease by a total amount ofΩ.

FIG. 16B illustrates an impedance trending graph 1600 of measurementdata taken by the electronic device during a transition phase 1630according to one embodiment. The transition phase 1630 is a phase whenthe trending data shifts from a low activity phase 1640 to a highactivity phase 1620. The electronic device can analyze the trending datato determine that the user has ceased a low activity action (such assleeping) and is now performing high activity action (such as running)In one embodiment, as the body of the user physiologically adjusts fromthe low activity phase 1640 to the high activity phase 1620, thetrending data can increase as a specified rate or a rate within aspecified range. Additionally or alternatively, as the body of the userphysiologically adjusts from the low activity phase 1640 to the highactivity phase 1620, the trending data can increase by a total amount ofΩ.

FIG. 16C illustrates an impedance trending graph 1600 of measurementdata taken by the electronic device during a transition phase 1630according to one embodiment. The transition phase 1630 is a phase whenthe trending data shifts from a low activity phase 1640 to a highactivity phase 1620 and back to the low activity phase 1640. Theelectronic device can analyze the trending data to determine that theuser has ceased a low activity action (such as sitting) and performedhigh activity action (such as running) and then returned to the lowactivity action (such as eating lunch and working). In one embodiment,as the body of the user physiologically adjusts from the low activityphase 1640 to the high activity phase 1620, the trending data canincrease as a specified rate or a rate within a specified range. Inanother embodiment, as the body of the user physiologically adjusts fromthe high activity phase 1620 to the low activity phase 1640, thetrending data can decrease as a specified rate or a rate within aspecified range. Additionally or alternatively, as the body of the userphysiologically adjusts from the high activity phase 1620 to the lowactivity phase 1640, the trending data can decrease by a total amount ofΩ.

FIG. 17A illustrates an impedance trending graph 1700 of measurementdata taken by the electronic device during a wetting phase 1780according to one embodiment. Before application of an electrode (such asan impedance pad) to a user's body surface, a membrane of the electrodemay be wetted with a wetting agent which permeates the membrane. Thewetting agent is electrically conductive or will become electricallyconductive upon contact with the user's skin. For example, the wettingagent can be saliva, conductive gel, water, and so forth. The wettingagent enhances a conductivity of the membrane to create a low resistancepath to current flowing between multiple electrolytes through the user'sskin. In one example, the membrane can include polytetrafluorethyleneembedded with fiberglass and a drop of water can be used as the wettingagent to wet the membrane to enhance its conductivity. In one example,water can be conductive due to the presence of impurities in the water.In another example, water can become conductive when it contacts bodysalts on a user's skin. After wetting the membrane a base of theelectrode may be applied to the skin. The electronic device candetermine that the trending data is in a wetting phase 1780, where theelectrode has been wetted and applied to the user's skin, because thetrending data has a no acclimation phase 1510 (FIG. 15A) or a minimalacclimation phase 1510. The wetting of the electrodes can shorten oreliminate the acclimation phase 1510 because the wetting agent can fillin the gaps 1596 (FIG. 15B) between surface 1598 and the electrodes1592, as discussed in the preceding paragraphs. The electronic devicecan analyze the trending data to determine that the electronic device isreceiving measurements and there is not the acclimation phase 1510 atthe beginning of the trending data.

FIG. 17B illustrates an impedance trending graph 1705 of measurementdata taken by the electronic device when an impedance pad of theelectronic device during a non-wet phase 1785 according to oneembodiment. As discussed in the preceding paragraphs, when electrodes ofthe electronic device are wetted, the conductivity between theelectrodes is enhanced. When the trending of the measurement dataindicates a non-wet phase 1785, the electronic device can analyze thetrending data to identify a period in the trending data where theimpedance level starts at zero or a negligible Ω and increases to anon-activity Ω level or an activity Ω level. For example, when theelectrodes are not wetted prior to attaching the electronic device to abody of a user, the trending data can start at 0Ω and can increase to8000Ω (an activity Ω level).

FIG. 17C illustrates an impedance trending graph 1790 of measurementdata with a transition phase 1795 according to one embodiment. In oneexample, the transition phase 1795 can occur when the measurement datatransitions from a high activity level to a low activity level. Inanother example, the transition phase 1795 can occur when themeasurement data transitions from a low activity level to a highactivity level. In another example, there can be a physiological delaybetween a user starting a high activity level event and the measurementdata reflecting the transition from the low activity level to the highactivity level. For example, a user can begin using an elliptical towork out but the transition period may occur at a later point. Thephysiological delay can be caused by a body of the user adjusting fromthe low activity level event to the high activity level event, such as adelay in perspiration between when the elliptical workout began and whenthe user began to perspire.

FIG. 17D illustrates an impedance trending graph 1796 of measurementdata with an acclimation phase 1798 and transition phases 1795 and 1799according to one embodiment. The acclimation phase 1798 can occur whenthe electronic device is initially attached to the user. The transitionphase 1795 can occur when the user begins a high activity level event.The transition phase 1799 can occur after the user ends their highactivity level event.

FIG. 18 illustrates an impedance trending graph 1800 for measurementdata with noise in the measurement data according to one embodiment. Theelectronic device can determine that there is noise in the measurementdata when a first data point in the measurement data jumps or skips froma first Ω level to a second Ω level, where the first Ω level and thesecond Ω level have a threshold difference in values. For example, whenthe trending graph 1800 has jagged or non-smooth trending, the jagged ornon-smooth trending can indicate that there is noise in the measurementdata. In one embodiment, when the electronic device determines there isnoise in the data, the electronic device can smooth the measurement datausing a smoothing function. In another embodiment, when the electronicdevice determines there is noise in the data, the electronic device canremove erroneous data points from the measurement data.

FIG. 19 illustrates an impedance trending graph 1900 for measurementdata with outlying data points 1910 in the measurement data according toone embodiment. The electronic device can determine that there areoutlying data points 1910 in the measurement data by comparing a valueof a first data point in the measurement data to a value of a seconddata point in the measurement data. When the difference between thefirst value and the second value exceeds a threshold difference betweenvalues, the electronic device can determine that the first data point orthe second data point is an outlying data point 1910. In one embodiment,to determine whether the first data point is an outlying data point, theelectronic device can compare the first data point to a third data pointthat precedes the first data point. In one example, when a differencebetween the first data point and the third data point is with athreshold range, the first data point is non-outlying data point 1920.In another example, when the difference between the first data point andthe third data point exceeds the threshold range, the first data pointis an outlying data point. The electronic device can make a similardetermination for the second data point by comparing the second datapoint with a fourth data point that proceeds the second data point. Whenthe electronic device identifies the outlying data point 1910, theelectronic device can remove the outlying data point 1910 from thetrending data.

In one example, the electronic device can identify the outlying datapoints using the same statistical methodology used to identify stepchanges in the trend data. Outlying data point elimination can beperformed by excluding all data points from a hydration calculation thatare outside a predetermined tolerance band for a given sample of datafor the statistical analysis technique used. In one example, thestatistical analysis technique is a standard deviation technique can beused to measure the scatter in a sample of data around the average forthe population of data. An outlying data point can be determined bydefining it as any point that is more than some number of standarddeviations from the average.

FIG. 20 illustrates an impedance trending graph 2000 for measurementdata with a discontinuity 2010 in the measurement data according to oneembodiment. The electronic device can identify a discontinuity 2010 inthe measurement data by comparing a value of a first data point 2020 inthe measurement data to a value of a second data point 2030 in themeasurement data. When the difference between the first value and thesecond value exceeds a threshold difference between values, theelectronic device can identify the discontinuity 2010 the first datapoint 2020 and the second data point 2030. To identify the discontinuity2010 the electronic device can compare the first data point 2020 to athird data point 2040 that precedes the first data point 2020. When adifference between the first data point 2020 and the third data point2040 is with a threshold range, the first data point 2020 is at a firsttrending level and is not an outlying or erroneous data point.Additionally, to identify the discontinuity 2010 the electronic devicecan compare the second data point 2030 to a fourth data point 2050 thatfollows the second data point 2030. When a difference between the seconddata point 2030 and the fourth data point 2050 is with a thresholdrange, the second data point 2030 is at a second trending level and isnot an outlying or erroneous data point. In this example, when thedifference between the first value and the second value exceeds athreshold difference between values and the first data point 2020 andthe second data point 2030 are not outlying or erroneous data points,there is a discontinuity 2010 between the first data point 2020 and thesecond data point 2030.

In one embodiment, the processing logic of the electronic device candetect abnormal shifts or changes in trended data. This detection ispreferably performed using a statistical control chart method, whichidentifies special cause variations in time-ordered data. In oneexample, parametric filtering can be used to prevent false hits andsingle parameter fluctuations.

In one embodiment, the control chart method can include one or moretrend parameters, determining if the latest, i.e., most recent, point inthe trend sample is greater than the upper control limit (UCL) or lowerthan the lower control limit (LCL). A flag can be set for each trendingdata that exceeds either of these limits. The flag should also includean indication of which limit, upper or lower, was exceeded. The flagsare used to perform parametric filtering for the trend analysis.

In one example all detected shifts, occurring at a single point in time,i.e. at the same trend point, are analyzed using a parametric filteringtechnique. If only a single parameter is flagged out of range, the eventis an outlying parameter and no hydration condition message isgenerated. If more than one parameter shifts at a single point in time,specific parameters and their direction of change are compared to afault template to determine if the shift in the trend data ischaracteristic of a known or recognizable cause. If the pattern matchesa known cause, then a trend alarm can be generated along with itscorresponding recommendation. Alternatively, an explanation of why theshift occurred can be generated. The general categories for recognizableshifts include, taking an electronic device on and off a body of theuser, adjusting an orientation of the electronic device, and so forth.When more than one trend parameter shifts occur but a pattern is notrecognized (i.e. not in the fault template), the trend parameter shiftsmay be placed on a watch list or ignored until discernable symptomsoccur.

FIG. 21 illustrates an impedance trending graph 2100 of measurement datawith device removal events 2110 and 2130 and a device re-attachmentevent 2120 according to one embodiment. The electronic device canidentify the device removal event 2110 or 2130 when the trending datajumps to 0Ω or near-zero Ωs. In one example, when the trending datajumps to 0Ω or near-zero Ωs the device removal event 2110 has occurred.In another example, the electronic device can compare a previous datapoint value prior to the jump in trending to a current data point valueafter the jump in the trending. When a difference between the previousdata point value and the current data point value exceeds a thresholdvalue, the device removal event 2110 or 2130 has occurred. When adifference between the previous data point value and the current datapoint value does not exceed the threshold value, the device removalevent 2110 or 2130 has not occurred. For example, when the trending dataslowly trends towards the 0Ω or near-zero Ωs, the electronic device maynot have been removed as the user is greatly dehydrated.

The electronic device can identify the device re-attachment event 2120when the trending data jumps from 0Ω or near-zero Ωs to a greater Ωvalue. In one example, when the trending data jumps from 0Ω or near-zeroΩs to the greater Ω value, the device re-attachment event 2120 hasoccurred. In another example, the electronic device can compare aprevious data point value prior to the jump in trending to a currentdata point value after the jump in the trending. When a differencebetween the previous data point value and the current data point valueexceeds a threshold value, the device re-attachment event 2120 hasoccurred. When a difference between the previous data point value andthe current data point value does not exceed the threshold value, thedevice re-attachment event 2120 has not occurred. For example, when thetrending data slowly trends towards the 0Ω or near-zero Ωs, theelectronic device may not have been removed as the user is greatlydehydrated.

FIG. 22 illustrates a graphical user interface (GUI) 2200 to settrending parameters of trending data according to one embodiment. TheGUI 2200 can display a trend graph section 2210. The trend graph section2210 can include a trend graph 2212 displaying a trending of measurementinformation. The trend graph 2212 can include trending zone 1 (2214) andtrending zone 2 (2216) identifying different zones in the trending dataof the trending graph 2212. For example, trending zone 1 (2214) canidentify a high activity level zone and trending zone 2 (2216) canidentify a low activity level zone. The trending zone 1 (2214) caninclude a lower boundary 2218 and an upper boundary 2220. The trendingzone 2 (2216) can include a lower boundary 2222 and an upper boundary2224. In one example, the electronic device can set the lower boundaries2218 and 2222 and the upper boundaries 2220 and 2224 for the respectivezones automatically (e.g., without user input) by identifying areas inthe trend graph 2212 that are relatively flat or relatively sloped. Inanother example, the electronic device can receive boundary informationfrom a user device (such as a mouse or keyboard) that manually sets thelower boundaries 2218 and 2222 and the upper boundaries 2220 and 2224.For example, the electronic device can receive, from an input device,adjustment information for the lower boundaries 2218 and 2222 and theupper boundaries 2220 and 2224. In one embodiment, the input device is amouse, keyboard, trackpad, touch screen, and a user can use the inputdevice to slide or move the lower boundary 2222 and the upper boundary2224 in the trend graph section 2210 to set zone 1.

In one embodiment, GUI 2200 can also include a parameter setting section2230 to adjust trending parameter settings of an electronic device. Theparameter setting section 2230 can include: a device type window 2232 toselect a type of device to be adjusted; a user window 2232 to select auser associated with the device to be adjusted; a zone type to bedisplayed for adjustment, such as all zones in the trending data; atrend type window 2236 to select types of trending data to be displayedfor adjustment; a duration window 2238 to select a duration of the trendgraph 2212 to display; and a precision window or sensitivity window 2240to set a sensitivity level of the electronic device. In one embodiment,the sensitivity level setting can determine how large trending zone 1(2214) or trending zone 2 (2216) may be. For example, zone 1 (2214) canbe set to have a max range of 1000 Ωs. In another example, zone 2 (2216)can be set to have a max range of 2000 Ωs. In another example, thesensitivity level setting can determine an amount the trending data candeviate from the boundaries 2218 and 2220 or the boundaries 2222 and2224 before the trending data is no longer in the respective zones.

The trend information can involve multiple levels of concern. One levelcould be related to the resident evaluating their behaviors. A secondlevel could relate to a pending critical condition that needs emergencyresponse. Trend information can be presented on a display. A user whomight be viewing trend information on the display can interact withtrending information via a touch screen or keyboard. Processing logiccan also incorporate multi-level access control software that can makeavailable to third parties, such as a medical resident, emergencypersonnel, caregivers, relatives or medical monitors.

The trend information can be stored over a period of time and can beused to establish baselines for the user. For example, when the trendingdata indicates a repetitive range or pattern in the data, the processinglogic can set a baseline using the repetitive range or pattern in thedata.

FIG. 23 illustrates a graphical user interface (GUI) 2300 to settrending parameters of trending data for a trending graph 2390 accordingto one embodiment. The trend graph 2390 can include trending zone 1(2350), trending zone 2 (2360), trending zone 3 (2370), and trendingzone 4 (2380) where each zone 1-4 identifies different zones in thetrending data of the trending graph 2390. The trend graph 2390 can be alow activity level graph. The remainder of the GUI 2300 is the same asthe GUI 2200 (FIG. 22) as indicated by the same reference numbers.

FIG. 24 illustrates a graphical user interface (GUI) 2400 to settrending parameters of trending data for a trending graph 2490 accordingto one embodiment. The trend graph 2490 can include trending zone 1(2450), trending zone 2 (2460), and trending zone 3 (2470), where eachzone 1-3 identifies different zones in the trending data of the trendinggraph 2490. The trend graph 2490 can be a high activity level graph. Theremainder of the GUI 2400 is the same as the GUI 2200 (FIG. 22) asindicated by the same reference numbers.

FIG. 25 provides an example illustration of a processing devicedisclosed herein, such as a user equipment (UE), a base station, anelectronic device (UMD), a mobile wireless device, a mobilecommunication device, a tablet, a handset, or other type of wirelessdevice according to one embodiment. The device may include one or moreantennas configured to communicate with a node or transmission station,such as a base station (BS), an evolved Node B (eNode B), a basebandunit (BBU), a remote radio head (RRH), a remote radio equipment (RRE), arelay station (RS), a radio equipment (RE), a remote radio unit (RRU), acentral processing module (CPM), or other type of wireless wide areanetwork (WWAN) access point. The device may be configured to communicateusing at least one wireless communication standard including 3GPP LTE,WiMAX, High-Speed Packet Access (HSPA), Bluetooth, and Wi-Fi. The devicemay communicate using separate antennas for each wireless communicationstandard or shared antennas for multiple wireless communicationstandards. The device may communicate in a wireless local area network(WLAN), a wireless personal area network (WPAN), and/or a WWAN.

FIG. 25 also provides an illustration of a microphone and one or morespeakers that may be used for audio input and output from the device.The display screen may be a liquid crystal display (LCD) screen, orother type of display screen such as an organic light emitting diode(OLED) display. The display screen may be configured as a touch screen.The touch screen may use capacitive, resistive, or another type of touchscreen technology. An application processor and a graphics processor maybe coupled to the internal memory to provide processing and displaycapabilities. A non-volatile memory port may also be used to providedata input/output options to a user. The non-volatile memory port mayalso be used to expand the memory capabilities of the wireless device. Akeyboard may be integrated with the wireless device or wirelesslyconnected to the wireless device to provide additional user input. Avirtual keyboard may also be provided using the touch screen.

Various techniques, or certain aspects or portions thereof, may take theform of program code (i.e., instructions) embodied in tangible media,such as floppy diskettes, CD-ROMs, hard drives, non-transitory computerreadable storage medium, or any other machine-readable storage mediumwherein, when the program code is loaded into and executed by a machine,such as a computer, the machine becomes an apparatus for practicing thevarious techniques. In the case of program code execution onprogrammable computers, the computing device may include a processor, astorage medium readable by the processor (including volatile andnon-volatile memory and/or storage elements), at least one input device,and at least one output device. The volatile and non-volatile memoryand/or storage elements may be a random access memory (RAM),electrically erasable programmable read-only memory (EEPROM), flashdrive or flash memory, optical drive, magnetic hard drive, or othermedium for storing electronic data. The base station and mobile stationmay also include a transceiver module, a counter module, a processingmodule, and/or a clock module or timer module. One or more programs thatmay implement or utilize the various techniques described herein may usean application programming interface (API), reusable controls, and thelike. Such programs may be implemented in a high-level procedural orobject-oriented programming language to communicate with a computersystem. However, the program(s) may be implemented in assembly ormachine language, if desired. In any case, the language may be acompiled or interpreted language, and combined with hardwareimplementations.

It should be understood that many of the functional units described inthis specification have been labeled as modules, in order to moreparticularly emphasize their implementation independence. For example, amodule may be implemented as a hardware circuit comprising custom VLSIcircuits or gate arrays, off-the-shelf semiconductors such as logicchips, transistors, or other discrete components. A module may also beimplemented in programmable hardware devices such as field programmablegate arrays, programmable array logic, programmable logic devices or thelike.

Modules may also be implemented in software for execution by varioustypes of processors. An identified module of executable code may, forinstance, comprise one or more physical or logical blocks of computerinstructions, which may, for instance, be organized as an object,procedure, or function. Nevertheless, the executables of an identifiedmodule need not be physically located together, but may comprisedisparate instructions stored in different locations which, when joinedlogically together, comprise the module and achieve the stated purposefor the module.

Indeed, a module of executable code may be a single instruction, or manyinstructions, and may even be distributed over several different codesegments, among different programs, and across several memory devices.Similarly, operational data may be identified and illustrated hereinwithin modules, and may be embodied in any suitable form and organizedwithin any suitable type of data structure. The operational data may becollected as a single data set, or may be distributed over differentlocations including over different storage devices, and may exist, atleast partially, merely as electronic signals on a system or network.The modules may be passive or active, including agents operable toperform desired functions.

Reference throughout this specification to “an example” means that aparticular feature, structure, or characteristic described in connectionwith the example is included in at least one embodiment of the presentinvention. Thus, appearances of the phrases “in an example” in variousplaces throughout this specification are not necessarily all referringto the same embodiment.

As used herein, a plurality of items, structural elements, compositionalelements, and/or materials may be presented in a common list forconvenience. However, these lists should be construed as though eachmember of the list is individually identified as a separate and uniquemember. Thus, no individual member of such list should be construed as ade facto equivalent of any other member of the same list solely based ontheir presentation in a common group without indications to thecontrary. In addition, various embodiments and example of the presentinvention may be referred to herein along with alternatives for thevarious components thereof. It is understood that such embodiments,examples, and alternatives are not to be construed as de factoequivalents of one another, but are to be considered as separate andautonomous representations of the present invention.

Furthermore, the described features, structures, or characteristics maybe combined in any suitable manner in one or more embodiments. In theforegoing description, numerous specific details are provided, such asexamples of layouts, distances, network examples, etc., to provide athorough understanding of embodiments of the invention. One skilled inthe relevant art will recognize, however, that the invention may bepracticed without one or more of the specific details, or with othermethods, components, layouts, etc. In other instances, well-knownstructures, materials, or operations are not shown or described indetail to avoid obscuring aspects of the invention.

While the foregoing examples are illustrative of the principles of thepresent invention in one or more particular applications, it will beapparent to those of ordinary skill in the art that numerousmodifications in form, usage and details of implementation may be madewithout the exercise of inventive faculty, and without departing fromthe principles and concepts of the invention. Accordingly, it is notintended that the invention be limited, except as by the claims setforth below.

Turning to FIG. 23 a block diagram of an exemplary computer systemformed with a processor that includes execution units to execute aninstruction, where one or more of the interconnects implement one ormore features in accordance with one example implementation of thepresent disclosure is illustrated. System 2300 includes a component,such as a processor 2302 to employ execution units including logic toperform algorithms for process data, in accordance with the presentdisclosure, such as in the example implementation described herein.System 2300 is representative of processing systems based on the PENTIUMIII™, PENTIUM 4™, Xeon™, Itanium, XScale™ and/or StrongARM™microprocessors available from Intel Corporation of Santa Clara, Calif.,although other systems (including PCs having other microprocessors,engineering workstations, set-top boxes and the like) may also be used.In one example implementation, sample system 2300 executes a version ofthe WINDOWS™ operating system available from Microsoft Corporation ofRedmond, Wash., although other operating systems (UNIX and Linux forexample), embedded software, and/or graphical user interfaces, may alsobe used. Thus, example implementations of the present disclosure are notlimited to any specific combination of hardware circuitry and software.

Example implementations are not limited to computer systems. Alternativeexample implementations of the present disclosure can be used in otherdevices such as handheld devices and embedded applications. Someexamples of handheld devices include cellular phones, Internet Protocoldevices, digital cameras, personal digital assistants (PDAs), andhandheld PCs. Embedded applications can include a microcontroller, adigital signal processor (DSP), system on a chip, network computers(NetPC), set-top boxes, network hubs, wide area network (WAN) switches,or any other system that can perform one or more instructions inaccordance with at least one example implementation.

Alternative example implementations of the present disclosure can beused in other devices, such as an electronic device. The electronicdevice may be connected (e.g., networked) to other machines in a LAN, anintranet, an extranet, or the Internet. The electronic device mayoperate in the capacity of a server or a client device in aclient-server network environment, or as a peer device in a peer-to-peer(or distributed) network environment. The electronic device may be apersonal computer (PC), a tablet PC, a set-top box (STB), a cellulartelephone, a smartphone, a web appliance, a server, a network router,switch or bridge, or any electronic device capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that electronic device. Further, while only a single electronicdevice is illustrated, the term “electronic device” shall also be takento include any collection of electronic devices that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein.

The system 2600 may correspond to the electronic device 110 (FIG. 1),the electronic device 220 (FIG. 2), the electronic device 410 (FIG. 4),the electronic device 510 (FIG. 5), the electronic device 610 (FIG. 6),the electronic device 710 (FIG. 7), the electronic device 910 (FIG. 9),or the electronic device 1010 (FIG. 10). The system 2600 may correspondto at least a portion of a cloud-based computer system.

In this illustrated example implementation, processor 2602 includes oneor more execution units 2608 to implement an algorithm that is toperform at least one instruction. One example implementation may bedescribed in the context of a single processor desktop or server system,but alternative example implementations may be included in amultiprocessor system. System 2600 is an example of a ‘hub’ systemarchitecture. The computer system 2600 includes a processor 2602 toprocess data signals. The processor 2602, as one illustrative example,includes a complex instruction set computer (CISC) microprocessor, areduced instruction set computing (RISC) microprocessor, a very longinstruction word (VLIW) microprocessor, a processor implementing acombination of instruction sets, or any other processor device, such asa digital signal processor, for example. The processor 2602 is coupledto a processor bus 2610 that transmits data signals between theprocessor 2602 and other components in the system 2600. The elements ofsystem 2600 (e.g. graphics accelerator 2612, memory controller hub 2616,memory 2620, I/O controller hub 2624, wireless transceiver 2626, FlashBIOS 2628, Network controller 2634, Audio controller 2636, Serialexpansion port 2638, I/O controller 2640, etc.) perform theirconventional functions that are well known to those familiar with theart.

In one example implementation, the processor 2602 includes a Level 1(L1) internal cache memory 2604. Depending on the architecture, theprocessor 2602 may have a single internal cache or multiple levels ofinternal caches. Other example implementations include a combination ofboth internal and external caches depending on the particularimplementation and needs. Register file 2606 is to store different typesof data in various registers including integer registers, floating pointregisters, vector registers, banked registers, shadow registers,checkpoint registers, status registers, and instruction pointerregister.

Execution unit 2608, including logic to perform integer and floatingpoint operations, also resides in the processor 2602. The processor2602, in one example implementation, includes a microcode (ucode) ROM tostore microcode, which when executed, is to perform algorithms forcertain macroinstructions or handle complex scenarios. Here, microcodeis potentially updateable to handle logic bugs/fixes for processor 2602.For one example implementation, execution unit 2608 includes logic tohandle a packed instruction set 2609. By including the packedinstruction set 2609 in the instruction set of a general-purposeprocessor 2602, along with associated circuitry to execute theinstructions, the operations used by many multimedia applications may beperformed using packed data in a general-purpose processor 2602. Thus,many multimedia applications are accelerated and executed moreefficiently by using the full width of a processor's data bus forperforming operations on packed data. This potentially eliminates theneed to transfer smaller units of data across the processor's data busto perform one or more operations, one data element at a time.

Alternate example implementations of an execution unit 2608 may also beused in microcontrollers, embedded processors, graphics devices, DSPs,and other types of logic circuits. System 2600 includes a memory 2620.Memory 2620 includes a dynamic random access memory (DRAM) device, astatic random access memory (SRAM) device, flash memory device, or othermemory device. Memory 2620 stores instructions and/or data representedby data signals that are to be executed by the processor 2602.

A system logic chip 2616 is coupled to the processor bus 2610 and memory2620. The system logic chip 2616 in the illustrated exampleimplementation is a memory controller hub (MCH). The processor 2602 cancommunicate to the MCH 2616 via a processor bus 2610. The MCH 2616provides a high bandwidth memory path 2618 to memory 2620 forinstruction and data storage and for storage of graphics commands, dataand textures. The MCH 2616 is to direct data signals between theprocessor 2602, memory 2620, and other components in the system 2600 andto bridge the data signals between processor bus 2610, memory 2620, andsystem I/O 2622. In some example implementations, the system logic chip2616 can provide a graphics port for coupling to a graphics controller2612. The MCH 2616 is coupled to memory 2620 through a memory interface2618. The graphics card 2612 is coupled to the MCH 2616 through anAccelerated Graphics Port (AGP) interconnect 2614.

System 2600 uses a proprietary hub interface bus 2622 to couple the MCH2616 to the I/O controller hub (ICH) 2630. The ICH 2630 provides directconnections to some I/O devices via a local I/O bus. The local I/O busis a high-speed I/O bus for connecting peripherals to the memory 2620,chipset, and processor 2602. Some examples are the audio controller,firmware hub (flash BIOS) 2628, wireless transceiver 2626, data storage2624, legacy I/O controller containing user input and keyboardinterfaces, a serial expansion port such as Universal Serial Bus (USB),and a network controller 2634. The data storage device 2624 can comprisea hard disk drive, a floppy disk drive, a CD-ROM device, a flash memorydevice, or other mass storage device.

For another example implementation of a system, an instruction inaccordance with one example implementation can be used with a system ona chip. One example implementation of a system on a chip comprises aprocessor and a memory. The memory for one such system is a flashmemory. The flash memory can be located on the same die as the processorand other system components. Additionally, other logic blocks such as amemory controller or graphics controller can also be located on a systemon a chip.

In the following description, numerous specific details are set forth,such as examples of specific types of processors and systemconfigurations, specific hardware structures, specific architectural andmicroarchitectural details, specific register configurations, specificinstruction types, specific system components, specificmeasurements/heights, specific processor pipeline stages and operationetc. in order to provide a thorough understanding of the presentdisclosure. It will be apparent, however, to one skilled in the art thatthese specific details need not be employed to practice the presentdisclosure. In other instances, well known components or methods, suchas specific and alternative processor architectures, specific logiccircuits/code for described algorithms, specific firmware code, specificinterconnect operation, specific logic configurations, specificmanufacturing techniques and materials, specific compilerimplementations, specific expression of algorithms in code, specificpower down and gating techniques/logic and other specific operationaldetails of computer system haven't been described in detail in order toavoid unnecessarily obscuring the present disclosure.

Although the following example implementations may be described withreference to energy conservation and energy efficiency in specificintegrated circuits, such as in computing platforms or microprocessors,other example implementations are applicable to other types ofintegrated circuits and logic devices. Similar techniques and teachingsof example implementations described herein may be applied to othertypes of circuits or semiconductor devices that may also benefit frombetter energy efficiency and energy conservation. For example, thedisclosed example implementations are not limited to desktop computersystems or Ultrabooks™. And may be also used in other devices, such ashandheld devices, tablets, other thin notebooks, systems on a chip (SOC)devices, and embedded applications. Some examples of handheld devicesinclude cellular phones, Internet protocol devices, digital cameras,personal digital assistants (PDAs), and handheld PCs. Embeddedapplications typically include a microcontroller, a digital signalprocessor (DSP), a system on a chip, network computers (NetPC), set-topboxes, network hubs, wide area network (WAN) switches, or any othersystem that can perform the functions and operations taught below.Moreover, the apparatus', methods, and systems described herein are notlimited to physical computing devices, but may also relate to softwareoptimizations for energy conservation and efficiency. As will becomereadily apparent in the description below, the example implementationsof methods, apparatus', and systems described herein (whether inreference to hardware, firmware, software, or a combination thereof) arevital to a ‘green technology’ future balanced with performanceconsiderations.

It is described that the system may be any kind of computer or embeddedsystem. The disclosed embodiments may especially be used for electronicdevices, electronic implants, sensory and control infrastructuredevices, controllers, supervisory control and data acquisition (SCADA)systems, or the like. Moreover, the apparatuses, methods, and systemsdescribed herein are not limited to physical computing devices, but mayalso relate to software optimizations for energy conservation andefficiency. As will become readily apparent in the description below,the embodiments of methods, apparatuses, and systems described herein(whether in reference to hardware, firmware, software, or a combinationthereof).

Although the following example implementations are described withreference to a processor, other example implementations are applicableto other types of integrated circuits and logic devices. Similartechniques and teachings of example implementations of the presentdisclosure can be applied to other types of circuits or semiconductordevices that can benefit from higher pipeline throughput and improvedperformance. The teachings of example implementations of the presentdisclosure are applicable to any processor or machine that performs datamanipulations. However, the present disclosure is not limited toprocessors or machines that perform 512-bit, 256-bit, 128-bit, 64-bit,32-bit, or 16-bit data operations and can be applied to any processorand machine in which manipulation or management of data is performed. Inaddition, the following description provides examples, and theaccompanying drawings show various examples for the purposes ofillustration. However, these examples should not be construed in alimiting sense as they are merely intended to provide examples ofexample implementations of the present disclosure rather than to providean exhaustive list of all possible implementations of exampleimplementations of the present disclosure.

Although the below examples describe instruction handling anddistribution in the context of execution units and logic circuits, otherexample implementations of the present disclosure can be accomplished byway of a data or instructions stored on a machine-readable, tangiblemedium, which when performed by a machine cause the machine to performfunctions consistent with at least one example implementation of thepresent disclosure. In one example implementation, functions associatedwith example implementations of the present disclosure are embodied inmachine-executable instructions. The instructions can be used to cause ageneral-purpose or special-purpose processor that is programmed with theinstructions to perform the steps of the present disclosure. Exampleimplementations of the present disclosure may be provided as a computerprogram product or software which may include a machine orcomputer-readable medium having stored thereon instructions which may beused to program a computer (or other electronic devices) to perform oneor more operations according to example implementations of the presentdisclosure. Alternatively, steps of example implementations of thepresent disclosure might be performed by specific hardware componentsthat contain fixed-function logic for performing the steps, or by anycombination of programmed computer components and fixed-functionhardware components.

Instructions used to program logic to perform example implementations ofthe present disclosure can be stored within a memory in the system, suchas DRAM, cache, flash memory, or other storage. Furthermore, theinstructions can be distributed via a network or by way of othercomputer readable media. Thus a machine-readable medium may include anymechanism for storing or transmitting information in a form readable bya machine (e.g., a computer), but is not limited to, floppy diskettes,optical disks, Compact Disc, Read-Only Memory (CD-ROMs), andmagneto-optical disks, Read-Only Memory (ROMs), Random Access Memory(RAM), Erasable Programmable Read-Only Memory (EPROM), ElectricallyErasable Programmable Read-Only Memory (EEPROM), magnetic or opticalcards, flash memory, or a tangible, machine-readable storage used in thetransmission of information over the Internet via electrical, optical,acoustical or other forms of propagated signals (e.g., carrier waves,infrared signals, digital signals, etc.). Accordingly, thecomputer-readable medium includes any type of tangible machine-readablemedium suitable for storing or transmitting electronic instructions orinformation in a form readable by a machine (e.g., a computer).

The embodiments of methods, hardware, software, firmware or code setforth above may be implemented via instructions or code stored on amachine-accessible, machine readable, computer accessible, or computerreadable medium which are executable by a processing element. Anon-transitory machine-accessible/readable medium includes any mechanismthat provides (i.e., stores and/or transmits) information in a formreadable by a machine, such as a computer or electronic system. Forexample, a non-transitory machine-accessible medium includesrandom-access memory (RAM), such as static RAM (SRAM) or dynamic RAM(DRAM); ROM; magnetic or optical storage medium; flash memory devices;electrical storage devices; optical storage devices; acoustical storagedevices; other form of storage devices for holding information receivedfrom transitory (propagated) signals (e.g., carrier waves, infraredsignals, digital signals); etc., which are to be distinguished from thenon-transitory mediums that may receive information therefrom.

Instructions used to program logic to perform embodiments of theinvention may be stored within a memory in the system, such as DRAM,cache, flash memory, or other storage. Furthermore, the instructions maybe distributed via a network or by way of other computer readable media.Thus a machine-readable medium may include any mechanism for storing ortransmitting information in a form readable by a machine (e.g., acomputer), but is not limited to, floppy diskettes, optical disks,Compact Disc, Read-Only Memory (CD-ROMs), and magneto-optical disks,Read-Only Memory (ROMs), Random Access Memory (RAM), ErasableProgrammable Read-Only Memory (EPROM), Electrically ErasableProgrammable Read-Only Memory (EEPROM), magnetic or optical cards, flashmemory, or a tangible, machine-readable storage used in the transmissionof information over the Internet via electrical, optical, acoustical orother forms of propagated signals (e.g., carrier waves, infraredsignals, digital signals, etc.). Accordingly, the computer-readablemedium includes any type of tangible machine-readable medium suitablefor storing or transmitting electronic instructions or information in aform readable by a machine (e.g., a computer)

The computer-readable storage medium may also be used to storeinstructions utilizing logic and/or a software library containingmethods that call the above applications. While the computer-readablestorage medium can be a single medium, the term “computer-readablestorage medium” should be taken to include a single medium or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store the one or more sets of instructions. Theterm “computer-readable storage medium” shall accordingly be taken toinclude, but not be limited to, solid-state memories, optical media, andmagnetic media.

A design may go through various stages, from creation to simulation tofabrication. Data representing a design may represent the design in anumber of manners. First, as is useful in simulations, the hardware maybe represented using a hardware description language or anotherfunctional description language. Additionally, a circuit level modelwith logic and/or transistor gates may be produced at some stages of thedesign process. Furthermore, most designs, at some stage, reach a levelof data representing the physical placement of various devices in thehardware model. In the case where conventional semiconductor fabricationtechniques are used, the data representing the hardware model may be thedata specifying the presence or absence of various features on differentmask layers for masks used to produce the integrated circuit. In anyrepresentation of the design, the data may be stored in any form of amachine-readable medium. A memory or a magnetic or optical storage suchas a disc may be the machine readable medium to store informationtransmitted via optical or electrical wave modulated or otherwisegenerated to transmit such information. When an electrical carrier waveindicating or carrying the code or design is transmitted, to the extentthat copying, buffering, or re-transmission of the electrical signal isperformed, a new copy is made. Thus, a communication provider or anetwork provider may store on a tangible, machine-readable medium, atleast temporarily, an article, such as information encoded into acarrier wave, embodying techniques of example implementations of thepresent disclosure.

In modern processors, a number of different execution units are used toprocess and execute a variety of code and instructions. Not allinstructions are created equal as some are quicker to complete whileothers can take a number of clock cycles to complete. The faster thethroughput of instructions, the better the overall performance of theprocessor. Thus it would be advantageous to have as many instructionsexecute as fast as possible. However, there are certain instructionsthat have greater complexity and require more in terms of execution timeand processor resources. For example, there are floating pointinstructions, load/store operations, data moves, etc.

As more computer systems are used in internet, text, and multimediaapplications, additional processor support has been introduced overtime. In one example implementation, an instruction set may beassociated with one or more computer architectures, including datatypes, instructions, register architecture, addressing modes, memoryarchitecture, interrupt and exception handling, and external input andoutput (I/O).

In one example implementation, the instruction set architecture (ISA)may be implemented by one or more micro-architectures, which includesprocessor logic and circuits used to implement one or more instructionsets. Accordingly, processors with different micro-architectures canshare at least a portion of a common instruction set. For example,Intel® Pentium 4 processors, Intel® Core™ processors, and processorsfrom Advanced Micro Devices, Inc. of Sunnyvale Calif. implement nearlyidentical versions of the x86 instruction set (with some extensions thathave been added with newer versions), but have different internaldesigns. Similarly, processors designed by other processor developmentcompanies, such as ARM Holdings, Ltd., MIPS, or their licensees oradopters, may share at least a portion a common instruction set, but mayinclude different processor designs. For example, the same registerarchitecture of the ISA may be implemented in different ways indifferent micro-architectures using new or well-known techniques,including dedicated physical registers, one or more dynamicallyallocated physical registers using a register renaming mechanism (e.g.,the use of a Register Alias Table (RAT), a Reorder Buffer (ROB) and aretirement register file. In one example implementation, registers mayinclude one or more registers, register architectures, register files,or other register sets that may or may not be addressable by a softwareprogrammer.

In one example implementation, an instruction may include one or moreinstruction formats. In one example implementation, an instructionformat may indicate various fields (number of bits, location of bits,etc.) to specify, among other things, the operation to be performed andthe operand(s) on which that operation is to be performed. Someinstruction formats may be further broken defined by instructiontemplates (or subformats). For example, the instruction templates of agiven instruction format may be defined to have different subsets of theinstruction format's fields and/or defined to have a given fieldinterpreted differently. In one example implementation, an instructionis expressed using an instruction format (and, if defined, in a givenone of the instruction templates of that instruction format) andspecifies or indicates the operation and the operands upon which theoperation will operate.

Scientific, financial, auto-vectorized general purpose, RMS(recognition, mining, and synthesis), and visual and multimediaapplications (e.g., 2D/3D graphics, image processing, videocompression/decompression, voice recognition algorithms and audiomanipulation) may require the same operation to be performed on a largenumber of data items. In one example implementation, Single InstructionMultiple Data (SIMD) refers to a type of instruction that causes aprocessor to perform an operation on multiple data elements. SIMDtechnology may be used in processors that can logically divide the bitsin a register into a number of fixed-sized or variable-sized dataelements, each of which represents a separate value. For example, in oneexample implementation, the bits in a 64-bit register may be organizedas a source operand containing four separate 16-bit data elements, eachof which represents a separate 16-bit value. This type of data may bereferred to as ‘packed’ data type or ‘vector’ data type, and operands ofthis data type are referred to as packed data operands or vectoroperands. In one example implementation, a packed data item or vectormay be a sequence of packed data elements stored within a singleregister, and a packed data operand or a vector operand may a source ordestination operand of an SIMD instruction (or ‘packed data instruction’or a ‘vector instruction’). In one example implementation, a SIMDinstruction specifies a single vector operation to be performed on twosource vector operands to generate a destination vector operand (alsoreferred to as a result vector operand) of the same or different size,with the same or different number of data elements, and in the same ordifferent data element order.

SIMD technology, such as that employed by the Intel® Core™ processorshaving an instruction set including x86, MMX™, Streaming SIMD Extensions(SSE), SSE2, SSE3, SSE4.1, and SSE4.2 instructions, ARM processors, suchas the ARM Cortex® family of processors having an instruction setincluding the Vector Floating Point (VFP) and/or NEON instructions, andMIPS processors, such as the Loongson family of processors developed bythe Institute of Computing Technology (ICT) of the Chinese Academy ofSciences, has enabled a significant improvement in applicationperformance (Core™ and MMX™ are registered trademarks or trademarks ofIntel Corporation of Santa Clara, Calif.).

In one example implementation, destination and source registers/data aregeneric terms to represent the source and destination of thecorresponding data or operation. In some example implementations, theymay be implemented by registers, memory, or other storage areas havingother names or functions than those depicted. For example, in oneexample implementation, “DEST1” may be a temporary storage register orother storage area, whereas “SRC1” and “SRC2” may be a first and secondsource storage register or other storage area, and so forth. In otherexample implementations, two or more of the SRC and DEST storage areasmay correspond to different data storage elements within the samestorage area (e.g., an SIMD register). In one example implementation,one of the source registers may also act as a destination register by,for example, writing back the result of an operation performed on thefirst and second source data to one of the two source registers servingas a destination registers.

A design may go through various stages, from creation to simulation tofabrication. Data representing a design may represent the design in anumber of manners. First, as is useful in simulations, the hardware maybe represented using a hardware description language or anotherfunctional description language. Additionally, a circuit level modelwith logic and/or transistor gates may be produced at some stages of thedesign process. Furthermore, most designs, at some stage, reach a levelof data representing the physical placement of various devices in thehardware model. In the case where conventional semiconductor fabricationtechniques are used, the data representing the hardware model may be thedata specifying the presence or absence of various features on differentmask layers for masks used to produce the integrated circuit. In anyrepresentation of the design, the data may be stored in any form of amachine-readable medium. A memory or a magnetic or optical storage suchas a disc may be the machine readable medium to store informationtransmitted via optical or electrical wave modulated or otherwisegenerated to transmit such information. When an electrical carrier waveindicating or carrying the code or design is transmitted, to the extentthat copying, buffering, or re-transmission of the electrical signal isperformed, a new copy is made. Thus, a communication provider or anetwork provider may store on a tangible, machine-readable medium, atleast temporarily, an article, such as information encoded into acarrier wave, embodying techniques of embodiments of the presentinvention.

A module as used herein refers to any combination of hardware, software,and/or firmware. As an example, a module includes hardware, such as amicrocontroller, associated with a non-transitory medium to store codeadapted to be executed by the micro-controller. Therefore, in referenceto a module, in one embodiment, refers to the hardware, which isspecifically configured to recognize and/or execute the code to be heldin a non-transitory medium. Furthermore, in another embodiment, use of amodule refers to the non-transitory medium including the code, which isspecifically adapted to be executed by the microcontroller to performpredetermined operations. And as may be inferred, in yet anotherembodiment, the term module (in this example) may refer to thecombination of the microcontroller and the non-transitory medium. Oftenmodule boundaries that are illustrated as separate commonly vary andpotentially overlap. For example, a first and a second module may sharehardware, software, firmware, or a combination thereof, whilepotentially retaining some independent hardware, software, or firmware.In one embodiment, use of the term logic includes hardware, such astransistors, registers, or other hardware, such as programmable logicdevices.

Use of the phrase ‘configured to,’ in one embodiment, refers toarranging, putting together, manufacturing, offering to sell, importingand/or designing an apparatus, hardware, logic, or element to perform adesignated or determined task. In this example, an apparatus or elementthereof that is not operating is still ‘configured to’ perform adesignated task if it is designed, coupled, and/or interconnected toperform said designated task. As a purely illustrative example, a logicgate may provide a 0 or a 1 during operation. But a logic gate‘configured to’ provide an enable signal to a clock does not includeevery potential logic gate that may provide a 1 or 0. Instead, the logicgate is one coupled in some manner that during operation the 1 or 0output is to enable the clock. Note once again that use of the term‘configured to’ does not require operation, but instead focus on thelatent state of an apparatus, hardware, and/or element, wherein thelatent state the apparatus, hardware, and/or element is designed toperform a particular task when the apparatus, hardware, and/or elementis operating.

Furthermore, use of the phrases ‘to,’ capable of/to,′ and or ‘operableto,’ in one embodiment, refers to some apparatus, logic, hardware,and/or element designed in such a way to enable use of the apparatus,logic, hardware, and/or element in a specified manner Note as above thatuse of to, capable to, or operable to, in one embodiment, refers to thelatent state of an apparatus, logic, hardware, and/or element, where theapparatus, logic, hardware, and/or element is not operating but isdesigned in such a manner to enable use of an apparatus in a specifiedmanner.

A value, as used herein, includes any known representation of a number,a state, a logical state, or a binary logical state. Often, the use oflogic levels, logic values, or logical values is also referred to as 1'sand 0's, which simply represents binary logic states. For example, a 1refers to a high logic level and 0 refers to a low logic level. In oneembodiment, a storage cell, such as a transistor or flash cell, may becapable of holding a single logical value or multiple logical values.However, other representations of values in computer systems have beenused. For example, the decimal number ten may also be represented as abinary value of 1010 and a hexadecimal letter A. Therefore, a valueincludes any representation of information capable of being held in acomputer system.

Moreover, states may be represented by values or portions of values. Asan example, a first value, such as a logical one, may represent adefault or initial state, while a second value, such as a logical zero,may represent a non-default state. In addition, the terms reset and set,in one embodiment, refer to a default and an updated value or state,respectively. For example, a default value potentially includes a highlogical value, i.e. reset, while an updated value potentially includes alow logical value, i.e. set. Note that any combination of values may beutilized to represent any number of states.

Reference throughout this specification to “one embodiment” or “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiment is includedin at least one embodiment of the present invention. Thus, theappearances of the phrases “in one embodiment” or “in an embodiment” invarious places throughout this specification are not necessarily allreferring to the same embodiment. Furthermore, the particular features,structures, or characteristics may be combined in any suitable manner inone or more embodiments.

In the foregoing specification, a detailed description has been givenwith reference to specific exemplary embodiments. It will, however, beevident that various modifications and changes may be made theretowithout departing from the broader spirit and scope of the invention asset forth in the appended claims. The specification and drawings are,accordingly, to be regarded in an illustrative sense rather than arestrictive sense. Furthermore, the foregoing use of embodiment andother exemplary language does not necessarily refer to the sameembodiment or the same example, but may refer to different and distinctembodiments, as well as potentially the same embodiment.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here and generally,conceived to be a self-consistent sequence of operations leading to adesired result. The operations are those requiring physicalmanipulations of physical quantities. Usually, though not necessarily,these quantities take the form of electrical or magnetic signals capableof being stored, transferred, combined, compared and otherwisemanipulated. It has proven convenient at times, principally for reasonsof common usage, to refer to these signals as bits, values, elements,symbols, characters, terms, numbers or the like. The blocks describedherein may be hardware, software, firmware or a combination thereof.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as “defining,” “receiving,” “determining,” “issuing,”“linking,” “associating,” “obtaining,” “authenticating,” “prohibiting,”“executing,” “requesting,” “communicating,” or the like, refer to theactions and processes of a computing system, or similar electroniccomputing device, that manipulates and transforms data represented asphysical (e.g., electronic) quantities within the computing system'sregisters and memories into other data similarly represented as physicalquantities within the computing system memories or registers or othersuch information storage, transmission or display devices.

The words “example” or “exemplary” are used herein to mean serving as anexample, instance or illustration. Any aspect or design described hereinas “example” or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion. As used in this application, the term “or” is intendedto mean an inclusive “or” rather than an exclusive “or.” That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Moreover, use of the term “an embodiment” or “one embodiment” or“an implementation” or “one implementation” throughout is not intendedto mean the same embodiment or implementation unless described as such.Also, the terms “first,” “second,” “third,” “fourth,” etc. as usedherein are meant as labels to distinguish among different elements andmay not necessarily have an ordinal meaning according to their numericaldesignation.

What is claimed is:
 1. A method comprising: measuring, by a sensor, aphysiological measurement of an individual to obtain trend datacorrelating to a hydration level of the individual; identifying, by aprocessing device coupled to the sensor, an acclimation period of theindividual subsequent to the individual attaching a wearable device to abody part of the individual, wherein: the acclimation period is aphysiological adjustment of the body part to the wearable device beingattached to the body part of the individual, wherein the acclimationperiod comprises: a first conductance between a first impedance pad anda second impedance pad of the wearable device based on a first distancebetween the body part and the first impedance pad; and a secondconductance between the first impedance pad and the second impedance padof the wearable device based on a second distance between the body partand the first impedance pad, wherein: the first distance is greater thanthe second distance; and the second conductance is greater than thefirst conductance; and the acclimation period is captured by the sensorduring a first period of time during the physiological measurement;identifying, by the processing device, a first portion of the trend datathat is within a variance range at a first measurement range, wherein:the first portion of the trend data is captured by the sensor during asecond period of time during the physiological measurement; and thefirst portion of the trend data is indicative of a first activity levelof the individual; identifying, by the processing device, a secondportion of the trend data that exceeds the variance range at a secondmeasurement range, wherein: the second portion of the trend data iscaptured by the sensor during a third period of time during thephysiological measurement; and the second portion of the trend data isindicative of a second activity level of the individual, wherein thesecond activity level is different than the first activity level;identifying, by the processing device, a third portion of the trend datathat is within the variance range at a third measurement range, wherein:the third portion of the trend data is captured by the sensor during afourth period of time between the second period of time and the thirdperiod of time, wherein the fourth period of time separates the secondperiod of time and the third period of time; and the third portion ofthe trend data is indicative of a physiological transition by a body ofthe individual from the first activity level to the second activitylevel; removing, by the processing device, the acclimation period of thetrend data from the trend data; removing, by the processing device, thethird portion of the trend data from the trend data; identifying, by theprocessing device, a trend parameter for at least one of the firstportion of the trend data or the second portion of the trend data, thetrend parameter indicating a transition of the hydration level of theindividual from a first hydration level to a second hydration level; andproviding a notification of the transition from the first hydrationlevel to the second hydration level to the individual.
 2. The method ofclaim 1, wherein the sensor comprises at least one of a pulse oximetersensor, an electrocardiography (ECG) sensor, a fluid level sensor, anoxygen saturation sensor, a body temperature sensor, an ambienttemperature sensor, a plethysmograph sensor, a respiration sensor, abreath sensor, a cardiac sensor, a heart rate sensor, an impedancesensor, an optical sensor, a spectrographic sensor, a humidity sensor,an altitude sensor, a barometer, a global positioning system (GPS)sensor, a triangulation sensor, a location sensor, a gyroscope sensor, avibration sensor, an accelerometer sensor, a three-dimensional (3D)accelerometer sensor, a force sensor, a pedometer, a strain gauge, amagnetometer, or a geomagnetic field sensor.
 3. The method of claim 1,wherein the physiological measurement comprises at least one of ahydration level measurement, a heart rate measurement, a blood pressuremeasurement, an oxygen level measurement, or a temperature measurement.4. The method of claim 1, wherein the sensor comprises at least one of aphysiological sensor, an environmental sensor, or a Newtonian sensor. 5.The method of claim 1, further comprising identifying a baselinevariance range for the physiological measurement.
 6. The method of claim1, wherein the trend parameter is a coefficient of variation indicatingan amount of variability relative to a reference value for thephysiological measurement.
 7. A wearable user measurement device (UMD)comprising: a sensor operable to measure a physiological measurement ofan individual, wherein the sensor comprises a porous material facing abody part of the individual; and a processor operatively coupled to thesensor, wherein the processor is to: measure the physiologicalmeasurement of the individual with the sensor to obtain trend datacorrelating to a hydration level of the individual; identify anacclimation period of the individual subsequent to the individualattaching a wearable device to the body part of the individual, whereinthe acclimation period is: a physiological adjust of the body part tothe wearable device being attached to the body part of the individual,wherein the physical adjust comprises filling in gaps of the porousmaterial of the sensor; and the acclimation period is during a firstperiod of time during the physiological measurement; identify a firstportion of the trend data that is within a variance range at a firstmeasurement range, wherein: the first portion of the trend data iscaptured by the sensor during a second period of time during thephysiological measurement; and the first portion of the trend data isindicative of a first activity level of the individual; identify asecond portion of the trend data that exceeds the variance range at asecond measurement range, wherein: the second portion of the trend datais captured by the during a third period of time time during thephysiological measurement; and the second portion of the trend data isindicative of a second activity level of the individual, wherein thesecond activity level is different than the first activity level;identify a third portion of the trend data that is within the variancerange at a third measurement range, wherein: the second portion of thetrend data is captured by the sensor during a fourth period of timebetween the second period of time, wherein the fourth period of timeseparates the second period of time and the third period of time duringthe physiological measurement; and the second portion of the trend datais indicative of a physiological transition by a body of the individualfrom the first activity level to the second activity level; remove theacclimation period of the trend data from the trend data; and remove thesecond portion of the trend data from the trend data, wherein the secondportion of the trend data is an acclimation phase of a body of theindividual becoming acclimated to wearing the UMD.
 8. The wearable UMDof claim 7, wherein the sensor comprises at least one of a pulseoximeter sensor, an electrocardiography (ECG) sensor, a fluid levelsensor, an oxygen saturation sensor, a body temperature sensor, anambient temperature sensor, a plethysmograph sensor, a respirationsensor, a breath sensor, a cardiac sensor, a heart rate sensor, animpedance sensor, an optical sensor, a spectrographic sensor, a humiditysensor, an altitude sensor, a barometer, a global positioning system(GPS) sensor, a triangulation sensor, a location sensor, a gyroscopesensor, a vibration sensor, an accelerometer sensor, a three-dimensional(3D) accelerometer sensor, a force sensor, a pedometer, a strain gauge,a magnetometer, or a geomagnetic field sensor.
 9. The wearable UMD ofclaim 7, wherein the physiological measurement comprises at least one ofa hydration level measurement, a heart rate measurement, a bloodpressure measurement, an oxygen level measurement, or a temperaturemeasurement.
 10. The wearable UMD of claim 7, wherein the sensorcomprises at least one of a physiological sensor, an environmentalsensor, or a Newtonian sensor.
 11. The wearable UMD of claim 7, whereinthe processor is further to determine a baseline variance range for theindividual.
 12. The wearable UMD of claim 7, wherein the processor isfurther to identify a trend parameter for at least one of the firstportion of the trend data or the third portion of the trend data, thetrend parameter indicating a transition of the hydration level of theindividual from a first hydration level to a second hydration level,wherein the trend parameter is a coefficient of variation indicating anamount of variability relative to a reference value.
 13. An apparatuscomprising: a sensor interface; and a processing element coupled to thesensor interface, wherein the processing element is operable to: measurea physiological measurement of an individual with a sensor to obtaintrend data correlating to a hydration level of the individual; identifyan acclimation period of the individual subsequent to the individualattaching a wearable device to a body part of the individual, wherein:the acclimation period is a physiological adjust of the body part to thewearable device being attached to the body part of the individual; theacclimation period is captured by the sensor during a first period oftime; the wearable device applies pressure to a surface of the body partengaged by the wearable device when the wearable device is attached tothe body part; and the physiological adjust comprises the surface of thebody part engaged by the wearable device compensating to the pressureapplied to the surface of the body part by the wearable device; identifya first portion of the trend data that is within a variance range at afirst measurement range, wherein: the first portion of the trend data iscaptured by the sensor at a second point in time; and the first portionof the trend data is indicative of a first activity level of theindividual; identify a second portion of the trend data that exceeds thevariance range at a second measurement range, wherein: the secondportion of the trend data is captured by the sensor at a third period oftime; and the second portion of the trend data is indicative of a secondactivity level of the individual, wherein the second activity level isdifferent than the first activity level; identify a third portion of thetrend data that is within the variance range at a third measurementrange, wherein: the third portion of the trend data is captured by thesensor at a fourth period of time between the second period of time andthe third period of time; and the third portion of the trend data isindicative of a physiological transition by a body of the individualfrom the first activity level to the second activity level; remove theacclimation period of the trend data from the trend data; and tag thesecond portion of the trend data in the trend data with a tag.
 14. Theapparatus of claim 13, wherein the physiological measurement comprisesat least one of a hydration level measurement, a heart rate measurement,a blood pressure measurement, an oxygen level measurement, or atemperature measurement.
 15. The apparatus of claim 13, wherein thesensor comprises at least one of a physiological sensor, anenvironmental sensor, and a Newtonian sensor.
 16. The apparatus of claim13, further comprising displaying a user interface wherein at least aportion of the user interface shows the first portion of the trend data,the second portion of the trend data with the tag, and the third portionof the trend data.
 17. The apparatus of claim 13, wherein the variancerange is a coefficient of variation indicating an amount of variabilityrelative to a reference value.
 18. The method of claim 1, wherein thesecond portion of the trend data is indicative of the body of individualadjusting to an environmental change or a physiological change.
 19. Theapparatus of claim 13, wherein the second portion of the trend data isindicative of the individual adjusting to an environmental change or aphysiological change.
 20. The method of claim 7, wherein the secondportion of the trend data is indicative of the body of individualadjusting to an environmental change or a physiological change.